SAW Manual
The Software Analysis Workbench (SAW) is a tool for constructing mathematical models of the computational behavior of software, transforming these models, and proving properties about them.
SAW can currently construct models of a subset of programs written in Cryptol, LLVM (and therefore C), and JVM (and therefore Java). SAW also has experimental, incomplete support for MIR (and therefore Rust). The models take the form of typed functional programs, so in a sense SAW can be considered a translator from imperative programs to their functional equivalents. Various external proof tools, including a variety of SAT and SMT solvers, can be used to prove properties about the functional models. SAW can construct models from arbitrary Cryptol programs, and from C and Java programs that have fixed-size inputs and outputs and that terminate after a fixed number of iterations of any loop (or a fixed number of recursive calls). One common use case is to verify that an algorithm specification in Cryptol is equivalent to an algorithm implementation in C or Java.
The process of extracting models from programs, manipulating them, forming queries about them, and sending them to external provers is orchestrated using a special purpose language called SAWScript. SAWScript is a typed functional language with support for sequencing of imperative commands.
The rest of this document first describes how to use the SAW tool, saw, and outlines the structure of the SAWScript language and its relationship to Cryptol. It then presents the SAWScript commands that transform functional models and prove properties about them. Finally, it describes the specific commands available for constructing models from imperative programs.
Invoking SAW
The primary mechanism for interacting with SAW is through the saw executable included as part of the standard binary distribution. With no arguments, saw starts a read-evaluate-print loop (REPL) that allows the user to interactively evaluate commands in the SAWScript language. With one file name argument, it executes the specified file as a SAWScript program.
In addition to a file name, the saw executable accepts several command-line options:
-h, -?, --help
Print a help message.
-V, --version
Show the version of the SAWScript interpreter.
-c path, --classpath=path
Specify a colon-delimited list of paths to search for Java classes.
-i path, --import-path=path
Specify a colon-delimited list of paths to search for imports.
-t, --extra-type-checking
Perform extra type checking of intermediate values.
-I, --interactive
Run interactively (with a REPL). This is the default if no other arguments are specified.
-j path, --jars=path
Specify a colon-delimited list of paths to .jar files to search for Java classes.
-b path, --java-bin-dirs
Specify a colon-delimited list of paths to search for a Java executable.
-d num, --sim-verbose=num
Set the verbosity level of the Java and LLVM simulators.
-v num, --verbose=num
Set the verbosity level of the SAWScript interpreter.
--clean-mismatched-versions-solver-cache[=path]
Run the clean_mismatched_versions_solver_cache command on the solver cache at the given path, or if no path is given, the solver cache at the value of the SAW_SOLVER_CACHE_PATH environment variable, then exit. See the section Caching Solver Results for a description of the clean_mismatched_versions_solver_cache command and the solver caching feature in general.
SAW also uses several environment variables for configuration:
CRYPTOLPATH
Specify a colon-delimited list of directory paths to search for Cryptol imports (including the Cryptol prelude).
PATH
If the --java-bin-dirs option is not set, then the PATH will be searched to find a Java executable.
SAW_IMPORT_PATH
Specify a colon-delimited list of directory paths to search for imports.
SAW_JDK_JAR
Specify the path of the .jar file containing the core Java libraries. Note that that is not necessary if the --java-bin-dirs option or the PATH environment variable is used, as SAW can use this information to determine the location of the core Java libraries’ .jar file.
SAW_SOLVER_CACHE_PATH
Specify a path at which to keep a cache of solver results obtained during calls to certain tactics. A cache is not created at this path until it is needed. See the section Caching Solver Results for more detail about this feature.
On Windows, semicolon-delimited lists are used instead of colon-delimited lists.
Structure of SAWScript
A SAWScript program consists, at the top level, of a sequence of commands to be executed in order. Each command is terminated with a semicolon. For example, the print command displays a textual representation of its argument. Suppose the following text is stored in the file print.saw:
The command saw print.saw will then yield output similar to the following:
The same code can be run from the interactive REPL:
At the REPL, terminating semicolons can be omitted:
To make common use cases simpler, bare values at the REPL are treated as if they were arguments to print:
One SAWScript file can be included in another using the include command, which takes the name of the file to be included as an argument. For example:
Typically, included files are used to import definitions, not perform side effects like printing. However, as you can see, if any commands with side effects occur at the top level of the imported file, those side effects will occur during import.
Syntax
The syntax of SAWScript is reminiscent of functional languages such as Cryptol, Haskell and ML. In particular, functions are applied by writing them next to their arguments rather than by using parentheses and commas. Rather than writing f(x, y), write f x y.
Comments are written as in C, Java, and Rust (among many other languages). All text from // until the end of a line is ignored. Additionally, all text between /* and */ is ignored, regardless of whether the line ends.
Basic Types and Values
All values in SAWScript have types, and these types are determined and checked before a program runs (that is, SAWScript is statically typed). The basic types available are similar to those in many other languages.
The
Inttype represents unbounded mathematical integers. Integer constants can be written in decimal notation (e.g.,42), hexadecimal notation (0x2a), and binary (0b00101010). However, unlike many languages, integers in SAWScript are used primarily as constants. Arithmetic is usually encoded in Cryptol, as discussed in the next section.The Boolean type,
Bool, contains the valuestrueandfalse, like in many other languages. As with integers, computations on Boolean values usually occur in Cryptol.Values of any type can be aggregated into tuples. For example, the value
(true, 10)has the type(Bool, Int).Values of any type can also be aggregated into records, which are exactly like tuples except that their components have names. For example, the value
{ b = true, n = 10 }has the type{ b : Bool, n : Int }.A sequence of values of the same type can be stored in a list. For example, the value
[true, false, true]has the type[Bool].Strings of textual characters can be represented in the
Stringtype. For example, the value"example"has typeString.The “unit” type, written
(), is essentially a placeholder, similar tovoidin languages like C and Java. It has only one value, also written(). Values of type()convey no information. We will show in later sections several cases where this is useful.Functions are given types that indicate what type they consume and what type they produce. For example, the type
Int -> Boolindicates a function that takes anIntas input and produces aBoolas output. Functions with multiple arguments use multiple arrows. For example, the typeInt -> String -> Boolindicates a function in which the first argument is anInt, the second is aString, and the result is aBool. It is possible, but not necessary, to group arguments in tuples, as well, so the type(Int, String) -> Booldescribes a function that takes one argument, a pair of anIntand aString, and returns aBool.
SAWScript also includes some more specialized types that do not have straightforward counterparts in most other languages. These will appear in later sections.
Basic Expression Forms
One of the key forms of top-level command in SAWScript is a binding, introduced with the let keyword, which gives a name to a value. For example:
Bindings can have parameters, in which case they define functions. For instance, the following function takes one parameter and constructs a list containing that parameter as its single element.
Functions themselves are values and have types. The type of a function that takes an argument of type a and returns a result of type b is a -> b.
Function types are typically inferred, as in the example f above. In this case, because f only creates a list with the given argument, and because it is possible to create a list of any element type, f can be applied to an argument of any type. We say, therefore, that f is polymorphic. Concretely, we write the type of f as {a} a -> [a], meaning it takes a value of any type (denoted a) and returns a list containing elements of that same type. This means we can also apply f to 10:
However, we may want to specify that a function has a more specific type. In this case, we could restrict f to operate only on Int parameters.
This will work identically to the original f on an Int parameter:
However, it will fail for a String parameter:
Type annotations can be applied to any expression. The notation (e : t) indicates that expression e is expected to have type t and that it is an error for e to have a different type. Most types in SAWScript are inferred automatically, but specifying them explicitly can sometimes enhance readability.
Because functions are values, functions can return other functions. We make use of this feature when writing functions of multiple arguments. Consider the function g, similar to f but with two arguments:
Like f, g is polymorphic. Its type is {a} a -> a -> [a]. This means it takes an argument of type a and returns a function that takes an argument of the same type a and returns a list of a values. We can therefore apply g to any two arguments of the same type:
But type checking will fail if we apply it to two values of different types:
So far we have used two related terms, function and command, and we take these to mean slightly different things. A function is any value with a function type (e.g., Int -> [Int]). A command is any value with a special command type (e.g. TopLevel (), as shown below). These special types allow us to restrict command usage to specific contexts, and are also parameterized (like the list type). Most but not all commands are also functions.
The most important command type is the TopLevel type, indicating a command that can run at the top level (directly at the REPL, or as one of the top level commands in a script file). The print command has the type {a} a -> TopLevel (), where TopLevel () means that it is a command that runs in the TopLevel context and returns a value of type () (that is, no useful information). In other words, it has a side effect (printing some text to the screen) but doesn’t produce any information to use in the rest of the SAWScript program. This is the primary usage of the () type.
It can sometimes be useful to bind a sequence of commands together. This can be accomplished with the do { ... } construct. For example:
The bound value, print_two, has type TopLevel (), since that is the type of its last command.
Note that in the previous example the printing doesn’t occur until print_two directly appears at the REPL. The let expression does not cause those commands to run. The construct that runs a command is written using the <- operator. This operator works like let except that it says to run the command listed on the right hand side and bind the result, rather than binding the variable to the command itself. Using <- instead of let in the previous example yields:
Here, the print commands run first, and then print_two gets the value returned by the second print command, namely (). Any command run without using <- at either the top level of a script or within a do block discards its result. However, the REPL prints the result of any command run without using the <- operator.
In some cases it can be useful to have more control over the value returned by a do block. The return command allows us to do this. For example, say we wanted to write a function that would print a message before and after running some arbitrary command and then return the result of that command. We could write:
If we put this script in run.saw and run it with saw, we get something like:
Note that it ran the first print command, then the caller-specified command, then the second print command. The result stored in x at the end is the result of the return command passed in as an argument.
Other Basic Functions
Aside from the functions we have listed so far, there are a number of other operations for working with basic data structures and interacting with the operating system.
The following functions work on lists:
concat : {a} [a] -> [a] -> [a]takes two lists and returns the concatenation of the two.head : {a} [a] -> areturns the first element of a list.tail : {a} [a] -> [a]returns everything except the first element.length : {a} [a] -> Intcounts the number of elements in a list.null : {a} [a] -> Boolindicates whether a list is empty (has zero elements).nth : {a} [a] -> Int -> areturns the element at the given position, withnth l 0being equivalent tohead l.for : {m, a, b} [a] -> (a -> m b) -> m [b]takes a list and a function that runs in some command context. The passed command will be called once for every element of the list, in order. Returns a list of all of the results produced by the command.
For interacting with the operating system, we have:
get_opt : Int -> Stringreturns the command-line argument tosawat the given index. Argument 0 is always the name of thesawexecutable itself, and higher indices represent later arguments.exec : String -> [String] -> String -> TopLevel Stringruns an external program given, respectively, an executable name, a list of arguments, and a string to send to the standard input of the program. Theexeccommand returns the standard output from the program it executes and prints standard error to the screen.exit : Int -> TopLevel ()stops execution of the current script and returns the given exit code to the operating system.
Finally, there are a few miscellaneous functions and commands:
show : {a} a -> Stringcomputes the textual representation of its argument in the same way asprint, but instead of displaying the value it returns it as aStringvalue for later use in the program. This can be useful for constructing more detailed messages later.str_concat : String -> String -> Stringconcatenates twoStringvalues, and can also be useful withshow.time : {a} TopLevel a -> TopLevel aruns any otherTopLevelcommand and prints out the time it took to execute.with_time : {a} TopLevel a -> TopLevel (Int, a)returns both the original result of the timed command and the time taken to execute it (in milliseconds), without printing anything in the process.
The Term Type
Perhaps the most important type in SAWScript, and the one most unlike the built-in types of most other languages, is the Term type. Essentially, a value of type Term precisely describes all possible computations performed by some program. In particular, if two Term values are equivalent, then the programs that they represent will always compute the same results given the same inputs. We will say more later about exactly what it means for two terms to be equivalent, and how to determine whether two terms are equivalent.
Before exploring the Term type more deeply, it is important to understand the role of the Cryptol language in SAW.
Cryptol and its Role in SAW
Cryptol is a domain-specific language originally designed for the high-level specification of cryptographic algorithms. It is general enough, however, to describe a wide variety of programs, and is particularly applicable to describing computations that operate on streams of data of some fixed size.
In addition to being integrated into SAW, Cryptol is a standalone language with its own manual:
SAW includes deep support for Cryptol, and in fact requires the use of Cryptol for most non-trivial tasks. To fully understand the rest of this manual and to effectively use SAW, you will need to develop at least a rudimentary understanding of Cryptol.
The primary use of Cryptol within SAWScript is to construct values of type Term. Although Term values can be constructed from various sources, inline Cryptol expressions are the most direct and convenient way to create them.
Specifically, a Cryptol expression can be placed inside double curly braces ({{ and }}), resulting in a value of type Term. As a very simple example, there is no built-in integer addition operation in SAWScript. However, we can use Cryptol’s built-in integer addition operator within SAWScript as follows:
Although it printed out in the same way as an Int, it is important to note that t actually has type Term. We can see how this term is represented internally, before being evaluated, with the print_term function.
For the moment, it’s not important to understand what this output means. We show it only to clarify that Term values have their own internal structure that goes beyond what exists in SAWScript. The internal representation of Term values is in a language called SAWCore. The full semantics of SAWCore are beyond the scope of this manual.
The text constructed by print_term can also be accessed programmatically (instead of printing to the screen) using the show_term function, which returns a String. The show_term function is not a command, so it executes directly and does not need <- to bind its result. Therefore, the following will have the same result as the print_term command above:
Numbers are printed in decimal notation by default when printing terms, but the following two commands can change that behavior.
set_ascii : Bool -> TopLevel (), when passedtrue, makes subsequentprint_termorshow_termcommands print sequences of bytes as ASCII strings (and doesn’t affect printing of anything else).set_base : Int -> TopLevel ()prints all bit vectors in the given base, which can be between 2 and 36 (inclusive).
A Term that represents an integer (any bit vector, as affected by set_base) can be translated into a SAWScript Int using the eval_int : Term -> Int function. This function returns an Int if the Term can be represented as one, and fails at runtime otherwise.
Similarly, values of type Bit in Cryptol can be translated into values of type Bool in SAWScript using the eval_bool : Term -> Bool function:
Anything with sequence type in Cryptol can be translated into a list of Term values in SAWScript using the eval_list : Term -> [Term] function.
Finally, a list of Term values in SAWScript can be collapsed into a single Term with sequence type using the list_term : [Term] -> Term function, which is the inverse of eval_list.
In addition to being able to extract integer and Boolean values from Cryptol expressions, Term values can be injected into Cryptol expressions. When SAWScript evaluates a Cryptol expression between {{ and }} delimiters, it does so with several extra bindings in scope:
Any variable in scope that has SAWScript type
Boolis visible in Cryptol expressions as a value of typeBit.Any variable in scope that has SAWScript type
Intis visible in Cryptol expressions as a type variable. Type variables can be demoted to numeric bit vector values using the backtick (`) operator.Any variable in scope that has SAWScript type
Termis visible in Cryptol expressions as a value with the Cryptol type corresponding to the internal type of the term. The power of this conversion is that theTermdoes not need to have originally been derived from a Cryptol expression.
In addition to these rules, bindings created at the Cryptol level, either from included files or inside Cryptol quoting brackets, are visible only to later Cryptol expressions, and not as SAWScript variables.
To make these rules more concrete, consider the following examples. If we bind a SAWScript Int, we can use it as a Cryptol type variable. If we create a Term variable that internally has function type, we can apply it to an argument within a Cryptol expression, but not at the SAWScript level:
If f was a binding of a SAWScript variable to a Term of function type, we would get a different error:
One subtlety of dealing with Terms constructed from Cryptol is that because the Cryptol expressions themselves are type checked by the Cryptol type checker, and because they may make use of other Term values already in scope, they are not type checked until the Cryptol brackets are evaluated. So type errors at the Cryptol level may occur at runtime from the SAWScript perspective (though they occur before the Cryptol expressions are run).
So far, we have talked about using Cryptol value expressions. However, SAWScript can also work with Cryptol types. The most direct way to refer to a Cryptol type is to use type brackets: {| and |}. Any Cryptol type written between these brackets becomes a Type value in SAWScript. Some types in Cryptol are numeric (also known as size) types, and correspond to non-negative integers. These can be translated into SAWScript integers with the eval_size function. For example:
For non-numeric types, eval_size fails at runtime:
In addition to the use of brackets to write Cryptol expressions inline, several built-in functions can extract Term values from Cryptol files in other ways. The import command at the top level imports all top-level definitions from a Cryptol file and places them in scope within later bracketed expressions. This includes Cryptol foreign declarations. If a Cryptol implementation of a foreign function is present, then it will be used as the definition when reasoning about the function. Otherwise, the function will be imported as an opaque constant with no definition.
The cryptol_load command behaves similarly, but returns a CryptolModule instead. If any CryptolModule is in scope, its contents are available qualified with the name of the CryptolModule variable. A specific definition can be explicitly extracted from a CryptolModule using the cryptol_extract command:
cryptol_extract : CryptolModule -> String -> TopLevel Term
Transforming Term Values
The three primary functions of SAW are extracting models (Term values) from programs, transforming those models, and proving properties about models using external provers. So far we’ve shown how to construct Term values from Cryptol programs; later sections will describe how to extract them from other programs. Now we show how to use the various term transformation features available in SAW.
Rewriting
Rewriting a Term consists of applying one or more rewrite rules to it, resulting in a new Term. A rewrite rule in SAW can be specified in multiple ways:
as the definition of a function that can be unfolded,
as a term of Boolean type (or a function returning a Boolean) that is an equality statement, and
as a term of equality type with a body that encodes a proof that the equality in the type is valid.
In each case the term logically consists of two sides and describes a way to transform the left side into the right side. Each side may contain variables (bound by enclosing lambda expressions) and is therefore a pattern which can match any term in which each variable represents an arbitrary sub-term. The left-hand pattern describes a term to match (which may be a sub-term of the full term being rewritten), and the right-hand pattern describes a term to replace it with. Any variable in the right-hand pattern must also appear in the left-hand pattern and will be instantiated with whatever sub-term matched that variable in the original term.
For example, say we have the following Cryptol function:
We might for some reason want to replace multiplication by a power of two with a shift. We can describe this replacement using an equality statement in Cryptol (a rule of form 2 above):
Interpreting this as a rewrite rule, it says that for any 8-bit vector (call it y for now), we can replace y * 2 with y << 1. Using this rule to rewrite the earlier expression would then yield:
The general philosophy of rewriting is that the left and right patterns, while syntactically different, should be semantically equivalent. Therefore, applying a set of rewrite rules should not change the fundamental meaning of the term being rewritten. SAW is particularly focused on the task of proving that some logical statement expressed as a Term is always true. If that is in fact the case, then the entire term can be replaced by the term True without changing its meaning. The rewriting process can in some cases, by repeatedly applying rules that themselves are known to be valid, reduce a complex term entirely to True, which constitutes a proof of the original statement. In other cases, rewriting can simplify terms before sending them to external automated provers that can then finish the job. Sometimes this simplification can help the automated provers run more quickly, and sometimes it can help them prove things they would otherwise be unable to prove by applying reasoning steps (rewrite rules) that are not available to the automated provers.
In practical use, rewrite rules can be aggregated into Simpset values in SAWScript. A few pre-defined Simpset values exist:
empty_ss : Simpsetis the empty set of rules. Rewriting with it should have no effect, but it is useful as an argument to some of the functions that construct largerSimpsetvalues.basic_ss : Simpsetis a collection of rules that are useful in most proof scripts.cryptol_ss : () -> Simpsetincludes a collection of Cryptol-specific rules. Some of these simplify away the abstractions introduced in the translation from Cryptol to SAWCore, which can be useful when proving equivalence between Cryptol and non-Cryptol code. Leaving these abstractions in place is appropriate when comparing only Cryptol code, however, socryptol_ssis not included inbasic_ss.
The next set of functions can extend or apply a Simpset:
addsimp' : Term -> Simpset -> Simpsetadds a singleTermto an existing `Simpset.addsimps' : [Term] -> Simpset -> Simpsetadds a list ofTerms to an existingSimpset.rewrite : Simpset -> Term -> Termapplies aSimpsetto an existingTermto produce a newTerm.
To make this more concrete, we examine how the rewriting example sketched above, to convert multiplication into shift, can work in practice. We simplify everything with cryptol_ss as we go along so that the Terms don’t get too cluttered. First, we declare the term to be transformed:
Next, we declare the rewrite rule:
Finally, we apply the rule to the target term:
Note that addsimp' and addsimps' take a Term or list of Terms; these could in principle be anything, and are not necessarily terms representing logically valid equalities. They have ' suffixes because they are not intended to be the primary interface to rewriting. When using these functions, the soundness of the proof process depends on the correctness of these rules as a side condition.
The primary interface to rewriting uses the Theorem type instead of the Term type, as shown in the signatures for addsimp and addsimps.
addsimp : Theorem -> Simpset -> Simpsetadds a singleTheoremto aSimpset.addsimps : [Theorem] -> Simpset -> Simpsetadds severalTheoremvalues to aSimpset.
A Theorem is essentially a Term that is proven correct in some way. In general, a Theorem can be any statement, and may not be useful as a rewrite rule. However, if it has an appropriate shape it can be used for rewriting. In the “Proofs about Terms” section, we’ll describe how to construct Theorem values from Term values.
In the absence of user-constructed Theorem values, there are some additional built-in rules that are not included in either basic_ss and cryptol_ss because they are not always beneficial, but that can sometimes be helpful or essential. The cryptol_ss simpset includes rewrite rules to unfold all definitions in the Cryptol SAWCore module, but does not include any of the terms of equality type.
add_cryptol_defs :[String] -> Simpset -> Simpsetadds unfolding rules for functions with the given names from the SAWCoreCryptolmodule to the givenSimpset`.add_cryptol_eqs : [String] -> Simpset -> Simpsetadds the terms of equality type with the given names from the SAWCoreCryptolmodule to the givenSimpset.add_prelude_defs : [String] -> Simpset -> Simpsetadds unfolding rules from the SAWCorePreludemodule to aSimpset.add_prelude_eqs : [String] -> Simpset -> Simpsetadds equality-typed terms from the SAWCorePreludemodule to aSimpset.
Finally, it’s possible to construct a theorem from an arbitrary SAWCore expression (rather than a Cryptol expression), using the core_axiom function.
core_axiom : String -> Theoremcreates aTheoremfrom aStringin SAWCore syntax. AnyTheoremintroduced by this function is assumed to be correct, so use it with caution.
Folding and Unfolding
A SAWCore term can be given a name using the define function, and is then by default printed as that name alone. A named subterm can be “unfolded” so that the original definition appears again.
define : String -> Term -> TopLevel Termunfold_term : [String] -> Term -> Term
For example:
This process of folding and unfolding is useful both to make large terms easier for humans to work with and to make automated proofs more tractable. We’ll describe the latter in more detail when we discuss interacting with external provers.
In some cases, folding happens automatically when constructing Cryptol expressions. Consider the following example:
This illustrates that a bare expression in Cryptol braces gets translated directly to a SAWCore term. However, a Cryptol definition gets translated into a folded SAWCore term. In addition, because the second definition of t occurs at the Cryptol level, rather than the SAWScript level, it is visible only inside Cryptol braces. Definitions imported from Cryptol source files are also initially folded and can be unfolded as needed.
Other Built-in Transformation and Inspection Functions
In addition to the Term transformation functions described so far, a variety of others also exist.
beta_reduce_term : Term -> Termtakes any sub-expression of the form(\x -> t) vin the givenTermand replaces it with a transformed version oftin which all instances ofxare replaced byv.replace : Term -> Term -> Term -> TopLevel Termreplaces arbitrary subterms. A call toreplace x y treplaces any instance ofxinsidetwithy.
Assessing the size of a term can be particularly useful during benchmarking. SAWScript provides two mechanisms for this.
term_size : Term -> Intcalculates the number of nodes in the Directed Acyclic Graph (DAG) representation of aTermused internally by SAW. This is the most appropriate way of determining the resource use of a particular term.term_tree_size : Term -> Intcalculates how large aTermwould be if it were represented by a tree instead of a DAG. This can, in general, be much, much larger than the number returned byterm_size, and serves primarily as a way of assessing, for a specific term, how much benefit there is to the term sharing used by the DAG representation.
Finally, there are a few commands related to the internal SAWCore type of a Term.
check_term : Term -> TopLevel ()checks that the internal structure of aTermis well-formed and that it passes all of the rules of the SAWCore type checker.type : Term -> Typereturns the type of a particularTerm, which can then be used to, for example, construct a new fresh variable withfresh_symbolic.
Loading and Storing Terms
Most frequently, Term values in SAWScript come from Cryptol, JVM, or LLVM programs, or some transformation thereof. However, it is also possible to obtain them from various other sources.
parse_core : String -> Termparses aStringcontaining a term in SAWCore syntax, returning aTerm.read_core : String -> TopLevel Termis likeparse_core, but obtains the text from the given file and expects it to be in the simpler SAWCore external representation format, rather than the human-readable syntax shown so far.read_aig : String -> TopLevel Termreturns aTermrepresentation of an And-Inverter-Graph (AIG) file in AIGER format.read_bytes : String -> TopLevel Termreads a constant sequence of bytes from a file and represents it as aTerm. Its result will always have Cryptol type[n][8]for somen.
It is also possible to write Term values into files in various formats, including: AIGER (write_aig), CNF (write_cnf), SAWCore external representation (write_core), and SMT-Lib version 2 (write_smtlib2).
write_aig : String -> Term -> TopLevel ()write_cnf : String -> Term -> TopLevel ()write_core : String -> Term -> TopLevel ()write_smtlib2 : String -> Term -> TopLevel ()
Proofs about Terms
The goal of SAW is to facilitate proofs about the behavior of programs. It may be useful to prove some small fact to use as a rewrite rule in later proofs, but ultimately these rewrite rules come together into a proof of some higher-level property about a software system.
Whether proving small lemmas (in the form of rewrite rules) or a top-level theorem, the process builds on the idea of a proof script that is run by one of the top level proof commands.
prove_print : ProofScript () -> Term -> TopLevel Theoremtakes a proof script (which we’ll describe next) and aTerm. TheTermshould be of function type with a return value ofBool(Bitat the Cryptol level). It will then use the proof script to attempt to show that theTermreturnsTruefor all possible inputs. If it is successful, it will printValidand return aTheorem. If not, it will abort.sat_print : ProofScript () -> Term -> TopLevel ()is similar except that it looks for a single value for which theTermevaluates toTrueand prints out that value, returning nothing.prove_core : ProofScript () -> String -> TopLevel Theoremproves and returns aTheoremfrom a string in SAWCore syntax.
Automated Tactics
The simplest proof scripts just specify the automated prover to use. The ProofScript values abc and z3 select the ABC and Z3 theorem provers, respectively, and are typically good choices.
For example, combining prove_print with abc:
Similarly, sat_print will show that the function returns True for one specific input (which it should, since we already know it returns True for all inputs):
In addition to these, the boolector, cvc4, cvc5, mathsat, and yices provers are available. The internal decision procedure rme, short for Reed-Muller Expansion, is an automated prover that works particularly well on the Galois field operations that show up, for example, in AES.
In more complex cases, some pre-processing can be helpful or necessary before handing the problem off to an automated prover. The pre-processing can involve rewriting, beta reduction, unfolding, the use of provers that require slightly more configuration, or the use of provers that do very little real work.
Proof Script Diagnostics
During development of a proof, it can be useful to print various information about the current goal. The following tactics are useful in that context.
print_goal : ProofScript ()prints the entire goal in SAWCore syntax.print_goal_consts : ProofScript ()prints a list of unfoldable constants in the current goal.print_goal_depth : Int -> ProofScript ()takes an integer argument,n, and prints the goal up to depthn. Any elided subterms are printed with a...notation.print_goal_size : ProofScript ()prints the number of nodes in the DAG representation of the goal.
Rewriting in Proof Scripts
One of the key techniques available for completing proofs in SAWScript is the use of rewriting or transformation. The following commands support this approach.
simplify : Simpset -> ProofScript ()works just likerewrite, except that it works in aProofScriptcontext and implicitly transforms the current (unnamed) goal rather than taking aTermas a parameter.goal_eval : ProofScript ()will evaluate the current proof goal to a first-order combination of primitives.goal_eval_unint : [String] -> ProofScript ()works likegoal_evalbut avoids expanding or simplifying the given names.
Other Transformations
Some useful transformations are not easily specified using equality statements, and instead have special tactics.
beta_reduce_goal : ProofScript ()works likebeta_reduce_termbut on the current goal. It takes any sub-expression of the form(\x -> t) vand replaces it with a transformed version oftin which all instances ofxare replaced byv.unfolding : [String] -> ProofScript ()works likeunfold_termbut on the current goal.
Using unfolding is mostly valuable for proofs based entirely on rewriting, since the default behavior for automated provers is to unfold everything before sending a goal to a prover. However, with some provers it is possible to indicate that specific named subterms should be represented as uninterpreted functions.
unint_cvc4 : [String] -> ProofScript ()unint_cvc5 : [String] -> ProofScript ()unint_yices : [String] -> ProofScript ()unint_z3 : [String] -> ProofScript ()
The list of String arguments in these cases indicates the names of the subterms to leave folded, and therefore present as uninterpreted functions to the prover. To determine which folded constants appear in a goal, use the print_goal_consts function described above.
Ultimately, we plan to implement a more generic tactic that leaves certain constants uninterpreted in whatever prover is ultimately used (provided that uninterpreted functions are expressible in the prover).
Note that each of the unint_* tactics have variants that are prefixed with sbv_ and w4_. The sbv_-prefixed tactics make use of the SBV library to represent and solve SMT queries:
sbv_unint_cvc4 : [String] -> ProofScript ()sbv_unint_cvc5 : [String] -> ProofScript ()sbv_unint_yices : [String] -> ProofScript ()sbv_unint_z3 : [String] -> ProofScript ()
The w4_-prefixed tactics make use of the What4 library instead of SBV:
w4_unint_cvc4 : [String] -> ProofScript ()w4_unint_cvc5 : [String] -> ProofScript ()w4_unint_yices : [String] -> ProofScript ()w4_unint_z3 : [String] -> ProofScript ()
In most specifications, the choice of SBV versus What4 is not important, as both libraries are broadly compatible in terms of functionality. There are some situations where one library may outpeform the other, however, due to differences in how each library represents certain SMT queries. There are also some experimental features that are only supported with What4 at the moment, such as enable_lax_loads_and_stores.
Caching Solver Results
SAW has the capability to cache the results of tactics which call out to automated provers. This can save a considerable amount of time in cases such as proof development and CI, where the same proof scripts are often run repeatedly without changes.
This caching is available for all tactics which call out to automated provers at runtime: abc, boolector, cvc4, cvc5, mathsat, yices, z3, rme, and the family of unint tactics described in the previous section.
When solver caching is enabled and one of the tactics mentioned above is encountered, if there is already an entry in the cache corresponding to the call then the cached result is used, otherwise the appropriate solver is queried, and the result saved to the cache. Entries are indexed by a SHA256 hash of the exact query to the solver (ignoring variable names), any options passed to the solver, and the names and full version strings of all the solver backends involved (e.g. ABC and SBV for the abc tactic). This ensures cached results are only used when they would be identical to the result of actually running the tactic.
The simplest way to enable solver caching is to set the environment variable SAW_SOLVER_CACHE_PATH. With this environment variable set, saw and saw-remote-api will automatically keep an LMDB database at the given path containing the solver result cache. Setting this environment variable globally therefore creates a global, concurrency-safe solver result cache used by all newly created saw or saw-remote-api processes. Note that when this environment variable is set, SAW does not create a cache at the specified path until it is actually needed.
There are also a number of SAW commands related to solver caching.
set_solver_cache_pathis like settingSAW_SOLVER_CACHE_PATHfor the remainder of the current session, but opens an LMDB database at the specified path immediately. If a cache is already in use in the current session (i.e. through a prior call toset_solver_cache_pathor throughSAW_SOLVER_CACHE_PATHbeing set and the cache being used at least once) then all entries in the cache already in use will be copied to the new cache being opened.clean_mismatched_versions_solver_cachewill remove all entries in the solver result cache which were created using solver backend versions which do not match the versions in the current environment. This can be run after an update to clear out any old, unusable entries from the solver cache. This command can also be run directly from the command line through the--clean-mismatched-versions-solver-cachecommand-line option.print_solver_cacheprints to the console all entries in the cache whose SHA256 hash keys start with the given hex string. Providing an empty string results in all entries in the cache being printed.print_solver_cache_statsprints to the console statistics including the size of the solver cache, where on disk it is stored, and some counts of how often it has been used during the current session.
For performing more complicated database operations on the set of cached results, the file solver_cache.py is provided with the Python bindings of the SAW Remote API. This file implements a general-purpose Python interface for interacting with the LMDB databases kept by SAW for solver caching.
Below is an example of using solver caching with saw -v Debug. Only the relevant output is shown, the rest abbreviated with “…”.
Other External Provers
In addition to the built-in automated provers already discussed, SAW supports more generic interfaces to other arbitrary theorem provers supporting specific interfaces.
external_aig_solver : String -> [String] -> ProofScript ()supports theorem provers that can take input as a single-output AIGER file. The first argument is the name of the executable to run. The second argument is the list of command-line parameters to pass to that executable. Any element of this list equal to"%f"will be replaced with the name of the temporary AIGER file generated for the proof goal. The output from the solver is expected to be in DIMACS solution format.external_cnf_solver : String -> [String] -> ProofScript ()works similarly but for SAT solvers that take input in DIMACS CNF format and produce output in DIMACS solution format.
Offline Provers
For provers that must be invoked in more complex ways, or to defer proof until a later time, there are functions to write the current goal to a file in various formats, and then assume that the goal is valid through the rest of the script.
offline_aig : String -> ProofScript ()offline_cnf : String -> ProofScript ()offline_extcore : String -> ProofScript ()offline_smtlib2 : String -> ProofScript ()offline_unint_smtlib2 : [String] -> String -> ProofScript ()
These support the AIGER, DIMACS CNF, shared SAWCore, and SMT-Lib v2 formats, respectively. The shared representation for SAWCore is described in the saw-script repository. The offline_unint_smtlib2 command represents the folded subterms listed in its first argument as uninterpreted functions.
Finishing Proofs without External Solvers
Some proofs can be completed using unsound placeholders, or using techniques that do not require significant computation.
assume_unsat : ProofScript ()indicates that the current goal should be assumed to be unsatisfiable. This is an alias forassume_valid. Users should prefer to useadmitinstead.assume_valid : ProofScript ()indicates that the current goal should be assumed to be valid. Users should prefer to useadmitinsteadadmit : String -> ProofScript ()indicates that the current goal should be assumed to be valid without proof. The given string should be used to record why the user has decided to assume this proof goal.quickcheck : Int -> ProofScript ()runs the goal on the given number of random inputs, and succeeds if the result of evaluation is alwaysTrue. This is unsound, but can be helpful during proof development, or as a way to provide some evidence for the validity of a specification believed to be true but difficult or infeasible to prove.trivial : ProofScript ()states that the current goal should be trivially true. This tactic recognizes instances of equality that can be demonstrated by conversion alone. In particular it is able to proveEqTrue xgoals wherexreduces to the constant valueTrue. It fails if this is not the case.
Multiple Goals
The proof scripts shown so far all have a single implicit goal. As in many other interactive provers, however, SAWScript proofs can have multiple goals. The following commands can introduce or work with multiple goals. These are experimental and can be used only after enable_experimental has been called.
goal_apply : Theorem -> ProofScript ()will apply a given introduction rule to the current goal. This will result in zero or more new subgoals.goal_assume : ProofScript Theoremwill convert the first hypothesis in the current proof goal into a localTheoremgoal_insert : Theorem -> ProofScript ()will insert a givenTheoremas a new hypothesis in the current proof goal.goal_intro : String -> ProofScript Termwill introduce a quantified variable in the current proof goal, returning the variable as aTerm.goal_when : String -> ProofScript () -> ProofScript ()will run the given proof script only when the goal name contains the given string.goal_exact : Term -> ProofScript ()will attempt to use the given term as an exact proof for the current goal. This tactic will succeed whever the type of the given term exactly matches the current goal, and will fail otherwise.split_goal : ProofScript ()will split a goal of the formPrelude.and prop1 prop2into two separate goalsprop1andprop2.
Proof Failure and Satisfying Assignments
The prove_print and sat_print commands print out their essential results (potentially returning a Theorem in the case of prove_print). In some cases, though, one may want to act programmatically on the result of a proof rather than displaying it.
The prove and sat commands allow this sort of programmatic analysis of proof results. To allow this, they use two types we haven’t mentioned yet: ProofResult and SatResult. These are different from the other types in SAWScript because they encode the possibility of two outcomes. In the case of ProofResult, a statement may be valid or there may be a counter-example. In the case of SatResult, there may be a satisfying assignment or the statement may be unsatisfiable.
prove : ProofScript SatResult -> Term -> TopLevel ProofResultsat : ProofScript SatResult -> Term -> TopLevel SatResult
To operate on these new types, SAWScript includes a pair of functions:
caseProofResult : {b} ProofResult -> b -> (Term -> b) -> btakes aProofResult, a value to return in the case that the statement is valid, and a function to run on the counter-example, if there is one.caseSatResult : {b} SatResult -> b -> (Term -> b) -> bhas the same shape: it returns its first argument if the result represents an unsatisfiable statement, or its second argument applied to a satisfying assignment if it finds one.
AIG Values and Proofs
Most SAWScript programs operate on Term values, and in most cases this is the appropriate representation. It is possible, however, to represent the same function that a Term may represent using a different data structure: an And-Inverter-Graph (AIG). An AIG is a representation of a Boolean function as a circuit composed entirely of AND gates and inverters. Hardware synthesis and verification tools, including the ABC tool that SAW has built in, can do efficient verification and particularly equivalence checking on AIGs.
To take advantage of this capability, a handful of built-in commands can operate on AIGs.
bitblast : Term -> TopLevel AIGrepresents aTermas anAIGby “blasting” all of its primitive operations (things like bit-vector addition) down to the level of individual bits.load_aig : String -> TopLevel AIGloads anAIGfrom an external AIGER file.save_aig : String -> AIG -> TopLevel ()saves anAIGto an external AIGER file.save_aig_as_cnf : String -> AIG -> TopLevel ()writes anAIGout in CNF format for input into a standard SAT solver.
Symbolic Execution
Analysis of Java and LLVM within SAWScript relies heavily on symbolic execution, so some background on how this process works can help with understanding the behavior of the available built-in functions.
At the most abstract level, symbolic execution works like normal program execution except that the values of all variables within the program can be arbitrary expressions, potentially containing free variables, rather than concrete values. Therefore, each symbolic execution corresponds to some set of possible concrete executions.
As a concrete example, consider the following C program that returns the maximum of two values:
If you call this function with two concrete inputs, like this:
then it will assign the value 5 to r. However, we can also consider what it will do for arbitrary inputs. Consider the following example:
where a and b are variables with unknown values. It is still possible to describe the result of the max function in terms of a and b. The following expression describes the value of r:
where ite is the “if-then-else” mathematical function, which based on the value of the first argument returns either the second or third. One subtlety of constructing this expression, however, is the treatment of conditionals in the original program. For any concrete values of a and b, only one branch of the if statement will execute. During symbolic execution, on the other hand, it is necessary to execute both branches, track two different program states (each composed of symbolic values), and then merge those states after executing the if statement. This merging process takes into account the original branch condition and introduces the ite expression.
A symbolic execution system, then, is very similar to an interpreter that has a different notion of what constitutes a value and executes all paths through the program instead of just one. Therefore, the execution process is similar to that of a normal interpreter, and the process of generating a model for a piece of code is similar to building a test harness for that same code.
More specifically, the setup process for a test harness typically takes the following form:
Initialize or allocate any resources needed by the code. For Java and LLVM code, this typically means allocating memory and setting the initial values of variables.
Execute the code.
Check the desired properties of the system state after the code completes.
Accordingly, three pieces of information are particularly relevant to the symbolic execution process, and are therefore needed as input to the symbolic execution system:
The initial (potentially symbolic) state of the system.
The code to execute.
The final state of the system, and which parts of it are relevant to the properties being tested.
In the following sections, we describe how the Java and LLVM analysis primitives work in the context of these key concepts. We start with the simplest situation, in which the structure of the initial and final states can be directly inferred, and move on to more complex cases that require more information from the user.
Symbolic Termination
Above we described the process of executing multiple branches and merging the results when encountering a conditional statement in the program. When a program contains loops, the branch that chooses to continue or terminate a loop could go either way. Therefore, without a bit more information, the most obvious implementation of symbolic execution would never terminate when executing programs that contain loops.
The solution to this problem is to analyze the branch condition whenever considering multiple branches. If the condition for one branch can never be true in the context of the current symbolic state, there is no reason to execute that branch, and skipping it can make it possible for symbolic execution to terminate.
Directly comparing the branch condition to a constant can sometimes be enough to ensure termination. For example, in simple, bounded loops like the following, comparison with a constant is sufficient.
In this case, the value of i is always concrete, and will eventually reach the value 10, at which point the branch corresponding to continuing the loop will be infeasible.
As a more complex example, consider the following function:
The loop in this function can only be determined to symbolically terminate if the analysis takes into account algebraic rules about common multiples. Similarly, it can be difficult to prove that a base case is eventually reached for all inputs to a recursive program.
In this particular case, however, the code is guaranteed to terminate after a fixed number of iterations (where the number of possible iterations is a function of the number of bits in the integers being used). To show that the last iteration is in fact the last possible one, it’s necessary to do more than just compare the branch condition with a constant. Instead, we can use the same proof tools that we use to ultimately analyze the generated models to, early in the process, prove that certain branch conditions can never be true (i.e., are unsatisfiable).
Normally, most of the Java and LLVM analysis commands simply compare branch conditions to the constant True or False to determine whether a branch may be feasible. However, each form of analysis allows branch satisfiability checking to be turned on if needed, in which case functions like f above will terminate.
Next, we examine the details of the specific commands available to analyze JVM and LLVM programs.
Loading Code
The first step in analyzing any code is to load it into the system.
Loading LLVM
To load LLVM code, simply provide the location of a valid bitcode file to the llvm_load_module function.
llvm_load_module : String -> TopLevel LLVMModule
The resulting LLVMModule can be passed into the various functions described below to perform analysis of specific LLVM functions.
The LLVM bitcode parser should generally work with LLVM versions between 3.5 and 16.0, though it may be incomplete for some versions. Debug metadata has changed somewhat throughout that version range, so is the most likely case of incompleteness. We aim to support every version after 3.5, however, so report any parsing failures as on GitHub.
Loading Java
Loading Java code is slightly more complex, because of the more structured nature of Java packages. First, when running saw, three flags control where to look for classes:
The
-bflag takes the path where thejavaexecutable lives, which is used to locate the Java standard library classes and add them to the class database. Alternatively, one can put the directory wherejavalives on thePATH, which SAW will search if-bis not set.The
-jflag takes the name of a JAR file as an argument and adds the contents of that file to the class database.The
-cflag takes the name of a directory as an argument and adds all class files found in that directory (and its subdirectories) to the class database. By default, the current directory is included in the class path.
Most Java programs will only require setting the -b flag (or the PATH), as that is enough to bring in the standard Java libraries. Note that when searching the PATH, SAW makes assumptions about where the standard library classes live. These assumptions are likely to hold on JDK 7 or later, but they may not hold on older JDKs on certain operating systems. If you are using an old version of the JDK and SAW is unable to find a standard Java class, you may need to specify the location of the standard classes’ JAR file with the -j flag (or, alternatively, with the SAW_JDK_JAR environment variable).
Once the class path is configured, you can pass the name of a class to the java_load_class function.
java_load_class : String -> TopLevel JavaClass
The resulting JavaClass can be passed into the various functions described below to perform analysis of specific Java methods.
Java class files from any JDK newer than version 6 should work. However, support for JDK 9 and later is experimental. Verifying code that only uses primitive data types is known to work well, but there are some as-of-yet unresolved issues in verifying code involving classes such as String. For more information on these issues, refer to this GitHub issue.
Loading MIR
To load a piece of Rust code, first compile it to a MIR JSON file, as described in this section, and then provide the location of the JSON file to the mir_load_module function:
mir_load_module : String -> TopLevel MIRModule
SAW currently supports Rust code that can be built with a January 23, 2023 Rust nightly. If you encounter a Rust feature that SAW does not support, please report it on GitHub.
Notes on Compiling Code for SAW
SAW will generally be able to load arbitrary LLVM bitcode, JVM bytecode, and MIR JSON files, but several guidelines can help make verification easier or more likely to succeed.
Compiling LLVM
For generating LLVM with clang, it can be helpful to:
Turn on debugging symbols with
-gso that SAW can find source locations of functions, names of variables, etc.Optimize with
-O1so that the generated bitcode more closely matches the C/C++ source, making the results more comprehensible.Use
-fno-threadsafe-staticsto preventclangfrom emitting unnecessary pthread code.Link all relevant bitcode with
llvm-link(including, e.g., the C++ standard library when analyzing C++ code).
All SAW proofs include side conditions to rule out undefined behavior, and proofs will only succeed if all of these side conditions have been discharged. However the default SAW notion of undefined behavior is with respect to the semantics of LLVM, rather than C or C++. If you want to rule out undefined behavior according to the C or C++ standards, consider compiling your code with -fsanitize=undefined or one of the related flags1 to clang.
Generally, you’ll also want to use -fsanitize-trap=undefined, or one of the related flags, to cause the compiled code to use llvm.trap to indicate the presence of undefined behavior. Otherwise, the compiled code will call a separate function, such as __ubsan_handle_shift_out_of_bounds, for each type of undefined behavior, and SAW currently does not have built in support for these functions (though you could manually create overrides for them in a verification script).
Compiling Java
For Java, the only compilation flag that tends to be valuable is -g to retain information about the names of function arguments and local variables.
Compiling MIR
In order to verify Rust code, SAW analyzes Rust’s MIR (mid-level intermediate representation) language. In particular, SAW analyzes a particular form of MIR that the mir-json tool produces. You will need to intall mir-json and run it on Rust code in order to produce MIR JSON files that SAW can load (see this section).
For cargo-based projects, mir-json provides a cargo subcommand called cargo saw-build that builds a JSON file suitable for use with SAW. cargo saw-build integrates directly with cargo, so you can pass flags to it like any other cargo subcommand. For example:
Note that:
The full output of
cargo saw-buildhere is omitted. The important part is the.linked-mir.jsonfile that appears afterlinking X mir files into, as that is the JSON file that must be loaded with SAW.SAW_RUST_LIBRARY_PATHshould point to the the MIR JSON files for the Rust standard library.
mir-json also supports compiling individual .rs files through mir-json’s saw-rustc command. As the name suggests, it accepts all of the flags that rustc accepts. For example:
Notes on C++ Analysis
The distance between C++ code and LLVM is greater than between C and LLVM, so some additional considerations come into play when analyzing C++ code with SAW.
The first key issue is that the C++ standard library is large and complex, and tends to be widely used by C++ applications. To analyze most C++ code, it will be necessary to link your code with a version of the libc++ library2 compiled to LLVM bitcode. The wllvm program can3 be useful for this.
The C++ standard library includes a number of key global variables, and any code that touches them will require that they be initialized using llvm_alloc_global.
Many C++ names are slightly awkward to deal with in SAW. They may be mangled relative to the text that appears in the C++ source code. SAW currently only understands the mangled names. The llvm-nm program can be used to show the list of symbols in an LLVM bitcode file, and the c++filt program can be used to demangle them, which can help in identifying the symbol you want to refer to. In addition, C++ names from namespaces can sometimes include quote marks in their LLVM encoding. For example:
This can be mentioned in SAW by saying:
Finally, there is no support for calling constructors in specifications, so you will need to construct objects piece-by-piece using, e.g., llvm_alloc and llvm_points_to.
Direct Extraction
In the case of the max function described earlier, the relevant inputs and outputs are immediately apparent. The function takes two integer arguments, always uses both of them, and returns a single integer value, making no other changes to the program state.
In cases like this, a direct translation is possible, given only an identification of which code to execute. Two functions exist to handle such simple code. The first, for LLVM is the more stable of the two:
llvm_extract : LLVMModule -> String -> TopLevel Term
A similar function exists for Java, but is more experimental.
jvm_extract : JavaClass -> String -> TopLevel Term
Because of its lack of maturity, it (and later Java-related commands) must be enabled by running the enable_experimental command beforehand.
enable_experimental : TopLevel ()
The structure of these two extraction functions is essentially identical. The first argument describes where to look for code (in either a Java class or an LLVM module, loaded as described in the previous section). The second argument is the name of the method or function to extract.
When the extraction functions complete, they return a Term corresponding to the value returned by the function or method as a function of its arguments.
These functions currently work only for code that takes some fixed number of integral parameters, returns an integral result, and does not access any dynamically-allocated memory (although temporary memory allocated during execution is allowed).
Creating Symbolic Variables
The direct extraction process just discussed automatically introduces symbolic variables and then abstracts over them, yielding a SAWScript Term that reflects the semantics of the original Java, LLVM, or MIR code. For simple functions, this is often the most convenient interface. For more complex code, however, it can be necessary (or more natural) to specifically introduce fresh variables and indicate what portions of the program state they correspond to.
fresh_symbolic : String -> Type -> TopLevel Termis responsible for creating new variables in this context. The first argument is a name used for pretty-printing of terms and counter-examples. In many cases it makes sense for this to be the same as the name used within SAWScript, as in the following:
However, using the same name is not required.
The second argument to fresh_symbolic is the type of the fresh variable. Ultimately, this will be a SAWCore type; however, it is usually convenient to specify it using Cryptol syntax with the type quoting brackets {| and |}. For example, creating a 32-bit integer, as might be used to represent a Java int or an LLVM i32, can be done as follows:
Although symbolic execution works best on symbolic variables, which are “unbound” or “free”, most of the proof infrastructure within SAW uses variables that are bound by an enclosing lambda expression. Given a Term with free symbolic variables, we can construct a lambda term that binds them in several ways.
abstract_symbolic : Term -> Termfinds all symbolic variables in theTermand constructs a lambda expression binding each one, in some order. The result is a function of some number of arguments, one for each symbolic variable. It is the simplest but least flexible way to bind symbolic variables.
If there are multiple symbolic variables in the Term passed to abstract_symbolic, the ordering of parameters can be hard to predict. In some cases (such as when a proof is the immediate next step, and it’s expected to succeed) the order isn’t important. In others, it’s nice to have more control over the order.
lambda : Term -> Term -> Termis the building block for controlled binding. It takes two terms: the one to transform, and the portion of the term to abstract over. Generally, the firstTermis one obtained fromfresh_symbolicand the second is aTermthat would be passed toabstract_symbolic.
lambdas : [Term] -> Term -> Termallows you to list the order in which symbolic variables should be bound. Consider, for example, aTermwhich adds two symbolic variables:
We can turn t into a function that takes x1 followed by x2:
Or we can turn t into a function that takes x2 followed by x1:
Specification-Based Verification
The built-in functions described so far work by extracting models of code that can then be used for a variety of purposes, including proofs about the properties of the code.
When the goal is to prove equivalence between some LLVM, Java, or MIR code and a specification, however, a more declarative approach is sometimes convenient. The following sections describe an approach that combines model extraction and verification with respect to a specification. A verified specification can then be used as input to future verifications, allowing the proof process to be decomposed.
Running a Verification
Verification of LLVM is controlled by the llvm_verify command.
The first two arguments specify the module and function name to verify, as with llvm_verify. The third argument specifies the list of already-verified specifications to use for compositional verification (described later; use [] for now). The fourth argument specifies whether to do path satisfiability checking, and the fifth gives the specification of the function to be verified. Finally, the last argument gives the proof script to use for verification. The result is a proved specification that can be used to simplify verification of functions that call this one.
Similar commands are available for JVM programs:
And for MIR programs:
Running a MIR-based verification
(Note: API functions involving MIR verification require enable_experimental in order to be used. As such, some parts of this API may change before being finalized.)
The String supplied as an argument to mir_verify is expected to be a function identifier. An identifier is expected adhere to one of the following conventions:
<crate name>/<disambiguator>::<function path><crate name>::<function path>
Where:
<crate name>is the name of the crate in which the function is defined. (If you produced your MIR JSON file by compiling a single.rsfile withsaw-rustc, then the crate name is the same as the name of the file, but without the.rsfile extension.)<disambiguator>is a hash of the crate and its dependencies. In extreme cases, it is possible for two different crates to have identical crate names, in which case the disambiguator must be used to distinguish between the two crates. In the common case, however, most crate names will correspond to exactly one disambiguator, and you are allowed to leave out the/<disambiguator>part of theStringin this case. If you supply an identifier with an ambiguous crate name and omit the disambiguator, then SAW will raise an error.<function path>is the path to the function within the crate. Sometimes, this is as simple as the function name itself. In other cases, a function path may involve multiple segments, depending on the module hierarchy for the program being verified. For instance, areadfunction located incore/src/ptr/mod.rswill have the identifier:Where
coreis the crate name andptr::readis the function path, which has two segmentsptrandread. There are also some special forms of segments that appear for functions defined in certain language constructs. For instance, if a function is defined in animplblock, then it will have{impl}as one of its segments, e.g.,If you are in doubt about what the full identifier for a given function is, consult the MIR JSON file for your program.
Now we describe how to construct a value of type LLVMSetup (), JVMSetup (), or MIRSetup ().
Structure of a Specification
A specifications for Crucible consists of three logical components:
A specification of the initial state before execution of the function.
A description of how to call the function within that state.
A specification of the expected final value of the program state.
These three portions of the specification are written in sequence within a do block of type {LLVM,JVM,MIR}Setup. The command {llvm,jvm,mir}_execute_func separates the specification of the initial state from the specification of the final state, and specifies the arguments to the function in terms of the initial state. Most of the commands available for state description will work either before or after {llvm,jvm,mir}_execute_func, though with slightly different meaning, as described below.
Creating Fresh Variables
In any case where you want to prove a property of a function for an entire class of inputs (perhaps all inputs) rather than concrete values, the initial values of at least some elements of the program state must contain fresh variables. These are created in a specification with the {llvm,jvm,mir}_fresh_var commands rather than fresh_symbolic.
llvm_fresh_var : String -> LLVMType -> LLVMSetup Termjvm_fresh_var : String -> JavaType -> JVMSetup Termmir_fresh_var : String -> MIRType -> MIRSetup Term
The first parameter to both functions is a name, used only for presentation. It’s possible (though not recommended) to create multiple variables with the same name, but SAW will distinguish between them internally. The second parameter is the LLVM, Java, or MIR type of the variable. The resulting Term can be used in various subsequent commands.
Note that the second parameter to {llvm,jvm,mir}_fresh_var must be a type that has a counterpart in Cryptol. (For more information on this, refer to the “Cryptol type correspondence” section.) If the type does not have a Cryptol counterpart, the function will raise an error. If you do need to create a fresh value of a type that cannot be represented in Cryptol, consider using a function such as llvm_fresh_expanded_val (for LLVM verification) or mir_fresh_expanded_value (for MIR verification).
LLVM types are built with this set of functions:
llvm_int : Int -> LLVMTypellvm_alias : String -> LLVMTypellvm_array : Int -> LLVMType -> LLVMTypellvm_float : LLVMTypellvm_double : LLVMTypellvm_packed_struct : [LLVMType] -> LLVMTypellvm_struct : [LLVMType] -> LLVMType
Java types are built up using the following functions:
java_bool : JavaTypejava_byte : JavaTypejava_char : JavaTypejava_short : JavaTypejava_int : JavaTypejava_long : JavaTypejava_float : JavaTypejava_double : JavaTypejava_class : String -> JavaTypejava_array : Int -> JavaType -> JavaType
MIR types are built up using the following functions:
mir_adt : MIRAdt -> MIRTypemir_array : Int -> MIRType -> MIRTypemir_bool : MIRTypemir_char : MIRTypemir_i8 : MIRTypemir_i6 : MIRTypemir_i32 : MIRTypemir_i64 : MIRTypemir_i128 : MIRTypemir_isize : MIRTypemir_f32 : MIRTypemir_f64 : MIRTypemir_lifetime : MIRTypemir_ref : MIRType -> MIRTypemir_ref_mut : MIRType -> MIRTypemir_slice : MIRType -> MIRTypemir_str : MIRTypemir_tuple : [MIRType] -> MIRTypemir_u8 : MIRTypemir_u6 : MIRTypemir_u32 : MIRTypemir_u64 : MIRTypemir_u128 : MIRTypemir_usize : MIRType
Most of these types are straightforward mappings to the standard LLVM and Java types. The one key difference is that arrays must have a fixed, concrete size. Therefore, all analysis results are valid only under the assumption that any arrays have the specific size indicated, and may not hold for other sizes.
The llvm_int function takes an Int parameter indicating the variable’s bit width. For example, the C uint16_t and int16_t types correspond to llvm_int 16. The C bool type is slightly trickier. A bare bool type typically corresponds to llvm_int 1, but if a bool is a member of a composite type such as a pointer, array, or struct, then it corresponds to llvm_int 8. This is due to a peculiarity in the way Clang compiles bool down to LLVM. When in doubt about how a bool is represented, check the LLVM bitcode by compiling your code with clang -S -emit-llvm.
LLVM types can also be specified in LLVM syntax directly by using the llvm_type function.
llvm_type : String -> LLVMType
For example, llvm_type "i32" yields the same result as llvm_int 32.
The most common use for creating fresh variables is to state that a particular function should have the specified behaviour for arbitrary initial values of the variables in question. Sometimes, however, it can be useful to specify that a function returns (or stores, more about this later) an arbitrary value, without specifying what that value should be. To express such a pattern, you can also run llvm_fresh_var from the post state (i.e., after llvm_execute_func).
The SetupValue and JVMValue Types
Many specifications require reasoning about both pure values and about the configuration of the heap. The SetupValue type corresponds to values that can occur during symbolic execution, which includes both Term values, pointers, and composite types consisting of either of these (both structures and arrays).
The llvm_term, jvm_term, and mir_term functions create a SetupValue, JVMValue, or MIRValue, respectively, from a Term:
llvm_term : Term -> SetupValuejvm_term : Term -> JVMValuemir_term : Term -> MIRValue
The value that these functions return will have an LLVM, JVM, or MIR type corresponding to the Cryptol type of the Term argument. (For more information on this, refer to the “Cryptol type correspondence” section.) If the type does not have a Cryptol counterpart, the function will raise an error.
Cryptol type correspondence
The {llvm,jvm,mir}_fresh_var functions take an LLVM, JVM, or MIR type as an argument and produces a Term variable of the corresponding Cryptol type as output. Similarly, the {llvm,jvm,mir}_term functions take a Cryptol Term as input and produce a value of the corresponding LLVM, JVM, or MIR type as output. This section describes precisely which types can be converted to Cryptol types (and vice versa) in this way.
LLVM verification
The following LLVM types correspond to Cryptol types:
llvm_alias <name>: Corresponds to the same Cryptol type as the type used in the definition of<name>.llvm_array <n> <ty>: Corresponds to the Cryptol sequence[<n>][<cty>], where<cty>is the Cryptol type corresponding to<ty>.llvm_int <n>: Corresponds to the Cryptol word[<n>].llvm_struct [<ty_1>, ..., <ty_n>]andllvm_packed_struct [<ty_1>, ..., <ty_n>]: Corresponds to the Cryptol tuple(<cty_1>, ..., <cty_n>), where<cty_i>is the Cryptol type corresponding to<ty_i>for eachiranging from1ton.
The following LLVM types do not correspond to Cryptol types:
llvm_doublellvm_floatllvm_pointer
JVM verification
The following Java types correspond to Cryptol types:
java_array <n> <ty>: Corresponds to the Cryptol sequence[<n>][<cty>], where<cty>is the Cryptol type corresponding to<ty>.java_bool: Corresponds to the CryptolBittype.java_byte: Corresponds to the Cryptol[8]type.java_char: Corresponds to the Cryptol[16]type.java_int: Corresponds to the Cryptol[32]type.java_long: Corresponds to the Cryptol[64]type.java_short: Corresponds to the Cryptol[16]type.
The following Java types do not correspond to Cryptol types:
java_classjava_doublejava_float
MIR verification
The following MIR types correspond to Cryptol types:
mir_array <n> <ty>: Corresponds to the Cryptol sequence[<n>][<cty>], where<cty>is the Cryptol type corresponding to<ty>.mir_bool: Corresponds to the CryptolBittype.mir_char: Corresponds to the Cryptol[32]type.mir_i8andmir_u8: Corresponds to the Cryptol[8]type.mir_i16andmir_u16: Corresponds to the Cryptol[16]type.mir_i32andmir_u32: Corresponds to the Cryptol[32]type.mir_i64andmir_u64: Corresponds to the Cryptol[64]type.mir_i128andmir_u128: Corresponds to the Cryptol[128]type.mir_isizeandmir_usize: Corresponds to the Cryptol[32]type.mir_tuple [<ty_1>, ..., <ty_n>]: Corresponds to the Cryptol tuple(<cty_1>, ..., <cty_n>), where<cty_i>is the Cryptol type corresponding to<ty_i>for eachiranging from1ton.
The following MIR types do not correspond to Cryptol types:
mir_adtmir_f32mir_f64mir_refandmir_ref_mutmir_slicemir_str
Executing
Once the initial state has been configured, the {llvm,jvm,mir}_execute_func command specifies the parameters of the function being analyzed in terms of the state elements already configured.
llvm_execute_func : [SetupValue] -> LLVMSetup ()jvm_execute_func : [JVMValue] -> JVMSetup ()mir_execute_func : [MIRValue] -> MIRSetup ()
Return Values
To specify the value that should be returned by the function being verified use the {llvm,jvm,mir}_return command.
llvm_return : SetupValue -> LLVMSetup ()jvm_return : JVMValue -> JVMSetup ()mir_return : MIRValue -> MIRSetup ()
A First Simple Example
The commands introuduced so far are sufficient to verify simple programs that do not use pointers (or that use them only internally). Consider, for instance the C program that adds its two arguments together:
We can specify this function’s expected behavior as follows:
We can then compile the C file add.c into the bitcode file add.bc and verify it with ABC:
Compositional Verification
The primary advantage of the specification-based approach to verification is that it allows for compositional reasoning. That is, when proving properties of a given method or function, we can make use of properties we have already proved about its callees rather than analyzing them anew. This enables us to reason about much larger and more complex systems than otherwise possible.
The llvm_verify, jvm_verify, and mir_verify functions return values of type CrucibleMethodSpec, JVMMethodSpec, and MIRMethodSpec, respectively. These values are opaque objects that internally contain both the information provided in the associated LLVMSetup, JVMSetup, or MIRSetup blocks, respectively, and the results of the verification process.
Any of these MethodSpec objects can be passed in via the third argument of the ..._verify functions. For any function or method specified by one of these parameters, the simulator will not follow calls to the associated target. Instead, it will perform the following steps:
Check that all
llvm_points_toandllvm_precondstatements (or the corresponding JVM or MIR statements) in the specification are satisfied.Update the simulator state and optionally construct a return value as described in the specification.
More concretely, building on the previous example, say we have a doubling function written in terms of add:
It has a similar specification to add:
And we can verify it using what we’ve already proved about add:
In this case, doing the verification compositionally doesn’t save computational effort, since the functions are so simple, but it illustrates the approach.
Compositional Verification and Mutable Allocations
A common pitfall when using compositional verification is to reuse a specification that underspecifies the value of a mutable allocation. In general, doing so can lead to unsound verification, so SAW goes through great lengths to check for this.
Here is an example of this pitfall in an LLVM verification. Given this C code:
And the following SAW specifications:
Should SAW be able to verify the foo function against foo_spec using compositional verification? That is, should the following be expected to work?
A literal reading of side_effect_spec would suggest that the side_effect function allocates a_ptr but then does nothing with it, implying that foo returns its argument unchanged. This is incorrect, however, as the side_effect function actually changes its argument to point to 0, so the foo function ought to return 0 as a result. SAW should not verify foo against foo_spec, and indeed it does not.
The problem is that side_effect_spec underspecifies the value of a_ptr in its postconditions, which can lead to the potential unsoundness seen above when side_effect_spec is used in compositional verification. To prevent this source of unsoundness, SAW will invalidate the underlying memory of any mutable pointers (i.e., those declared with llvm_alloc, not llvm_alloc_global) allocated in the preconditions of compositional override that do not have a corresponding llvm_points_to statement in the postconditions. Attempting to read from invalidated memory constitutes an error, as can be seen in this portion of the error message when attempting to verify foo against foo_spec:
To fix this particular issue, add an llvm_points_to statement to side_effect_spec:
After making this change, SAW will reject foo_spec for a different reason, as it claims that foo returns its argument unchanged when it actually returns 0.
Note that invalidating memory itself does not constitute an error, so if the foo function never read the value of b after calling side_effect(&b), then there would be no issue. It is only when a function attempts to read from invalidated memory that an error is thrown. In general, it can be difficult to predict when a function will or will not read from invalidated memory, however. For this reason, it is recommended to always specify the values of mutable allocations in the postconditions of your specs, as it can avoid pitfalls like the one above.
The same pitfalls apply to compositional MIR verification, with a couple of key differences. In MIR verification, mutable references are allocated using mir_alloc_mut. Here is a Rust version of the pitfall program above:
Just like above, if you attempted to prove foo against foo_spec using compositional verification:
Then SAW would throw an error, as side_effect_spec underspecifies the value of a_ref in its postconditions. side_effect_spec can similarly be repaired by adding a mir_points_to statement involving a_ref in side_effect_spec’s postconditions.
MIR verification differs slightly from LLVM verification in how it catches underspecified mutable allocations when using compositional overrides. The LLVM memory model achieves this by invalidating the underlying memory in underspecified allocations. The MIR memory model, on the other hand, does not have a direct counterpart to memory invalidation. As a result, any MIR overrides must specify the values of all mutable allocations in their postconditions, even if the function that calls the override never uses the allocations.
To illustrate this point more finely, suppose that the foo function had instead been defined like this:
Here, it does not particularly matter what effects the side_effect function has on its argument, as foo will now return 42 regardless. Still, if you attempt to prove foo by using side_effect as a compositional override, then it is strictly required that you specify the value of side_effect’s argument in its postconditions, even though the answer that foo returns is unaffected by this. This is in contrast with LLVM verification, where one could get away without specifying side_effect’s argument in this example, as the invalidated memory in b would never be read.
Compositional Verification and Mutable Global Variables
Just like with local mutable allocations (see the previous section), specifications used in compositional overrides must specify the values of mutable global variables in their postconditions. To illustrate this using LLVM verification, here is a variant of the C program from the previous example that uses a mutable global variable a:
If we attempted to verify foo against this foo_spec specification using compositional verification:
Then SAW would reject it, as side_effect_spec does not specify what a’s value should be in its postconditions. Just as with local mutable allocations, SAW will invalidate the underlying memory in a, and subsequently reading from a in the foo function will throw an error. The solution is to add an llvm_points_to statement in the postconditions that declares that a’s value is set to 0.
The same concerns apply to MIR verification, where mutable global variables are referred to as static mut items. (See the MIR static items section for more information). Here is a Rust version of the program above:
Just as above, we can repair this by adding a mir_points_to statement in side_effect_spec’s postconditions that specifies that A is set to 0.
Recall from the previous section that MIR verification is stricter than LLVM verification when it comes to specifying mutable allocations in the postconditions of compositional overrides. This is especially true for mutable static items. In MIR verification, any compositional overrides must specify the values of all mutable static items in the entire program in their postconditions, even if the function that calls the override never uses the static items. For example, if the foo function were instead defined like this:
Then it is still required for side_effect_spec to specify what A’s value will be in its postconditions, despite the fact that this has no effect on the value that foo will return.
Specifying Heap Layout
Most functions that operate on pointers expect that certain pointers point to allocated memory before they are called. The llvm_alloc command allows you to specify that a function expects a particular pointer to refer to an allocated region appropriate for a specific type.
llvm_alloc : LLVMType -> LLVMSetup SetupValue
This command returns a SetupValue consisting of a pointer to the allocated space, which can be used wherever a pointer-valued SetupValue can be used.
In the initial state, llvm_alloc specifies that the function expects a pointer to allocated space to exist. In the final state, it specifies that the function itself performs an allocation.
When using the experimental Java implementation, separate functions exist for specifying that arrays or objects are allocated:
jvm_alloc_array : Int -> JavaType -> JVMSetup JVMValuespecifies an array of the given concrete size, with elements of the given type.jvm_alloc_object : String -> JVMSetup JVMValuespecifies an object of the given class name.
The experimental MIR implementation also has a mir_alloc function, which behaves similarly to llvm_alloc. mir_alloc creates an immutable reference, but there is also a mir_alloc_mut function for creating a mutable reference:
mir_alloc : MIRType -> MIRSetup MIRValuemir_alloc_mut : MIRType -> MIRSetup MIRValue
MIR tracks whether references are mutable or immutable at the type level, so it is important to use the right allocation command for a given reference type.
In LLVM, it’s also possible to construct fresh pointers that do not point to allocated memory (which can be useful for functions that manipulate pointers but not the values they point to):
llvm_fresh_pointer : LLVMType -> LLVMSetup SetupValue
The NULL pointer is called llvm_null in LLVM and jvm_null in JVM:
llvm_null : SetupValuejvm_null : JVMValue
One final, slightly more obscure command is the following:
llvm_alloc_readonly : LLVMType -> LLVMSetup SetupValue
This works like llvm_alloc except that writes to the space allocated are forbidden. This can be useful for specifying that a function should take as an argument a pointer to allocated space that it will not modify. Unlike llvm_alloc, regions allocated with llvm_alloc_readonly are allowed to alias other read-only regions.
Specifying Heap Values
Pointers returned by llvm_alloc, jvm_alloc_{array,object}, or mir_alloc{,_mut} don’t initially point to anything. So if you pass such a pointer directly into a function that tried to dereference it, symbolic execution will fail with a message about an invalid load. For some functions, such as those that are intended to initialize data structures (writing to the memory pointed to, but never reading from it), this sort of uninitialized memory is appropriate. In most cases, however, it’s more useful to state that a pointer points to some specific (usually symbolic) value, which you can do with the points-to family of commands.
LLVM heap values
LLVM verification primarily uses the llvm_points_to command:
llvm_points_to : SetupValue -> SetupValue -> LLVMSetup ()takes twoSetupValuearguments, the first of which must be a pointer, and states that the memory specified by that pointer should contain the value given in the second argument (which may be any type ofSetupValue).
When used in the final state, llvm_points_to specifies that the given pointer should point to the given value when the function finishes.
Occasionally, because C programs frequently reinterpret memory of one type as another through casts, it can be useful to specify that a pointer points to a value that does not agree with its static type.
llvm_points_to_untyped : SetupValue -> SetupValue -> LLVMSetup ()works likellvm_points_tobut omits type checking. Rather than omitting type checking across the board, we introduced this additional function to make it clear when a type reinterpretation is intentional. As an alternative, one may instead usellvm_cast_pointerto line up the static types.
JVM heap values
JVM verification has two categories of commands for specifying heap values. One category consists of the jvm_*_is commands, which allow users to directly specify what value a heap object points to. There are specific commands for each type of JVM heap object:
jvm_array_is : JVMValue -> Term -> JVMSetup ()declares that an array (the first argument) contains a sequence of values (the second argument).jvm_elem_is : JVMValue -> Int -> JVMValue -> JVMSetup ()declares that an array (the first argument) has an element at the given index (the second argument) containing the given value (the third argument).jvm_field_is : JVMValue -> String -> JVMValue -> JVMSetup ()declares that an object (the first argument) has a field (the second argument) containing the given value (the third argument).jvm_static_field_is : String -> JVMValue -> JVMSetup ()declares that a named static field (the first argument) contains the given value (the second argument). By default, the field name is assumed to belong to the same class as the method being specified. Static fields belonging to other classes can be selected using the<classname>.<fieldname>syntax in the first argument.
Another category consists of the jvm_modifies_* commands. Like the jvm_*_is commands, these specify that a JVM heap object points to valid memory, but unlike the jvm_*_is commands, they leave the exact value being pointed to as unspecified. These are useful for writing partial specifications for methods that modify some heap value, but without saying anything specific about the new value.
jvm_modifies_array : JVMValue -> JVMSetup ()jvm_modifies_elem : JVMValue -> Int -> JVMSetup ()jvm_modifies_field : JVMValue -> String -> JVMSetup ()jvm_modifies_static_field : String -> JVMSetup ()
MIR heap values
MIR verification has a single mir_points_to command:
mir_points_to : MIRValue -> MIRValue -> MIRSetup ()takes twoSetupValuearguments, the first of which must be a reference, and states that the memory specified by that reference should contain the value given in the second argument (which may be any type ofSetupValue).
Working with Compound Types
The commands mentioned so far give us no way to specify the values of compound types (arrays or structs). Compound values can be dealt with either piecewise or in their entirety.
llvm_elem : SetupValue -> Int -> SetupValueyields a pointer to an internal element of a compound value. For arrays, theIntparameter is the array index. Forstructvalues, it is the field index.llvm_field : SetupValue -> String -> SetupValueyields a pointer to a particular namedstructfield, if debugging information is available in the bitcode.
Either of these functions can be used with llvm_points_to to specify the value of a particular array element or struct field. Sometimes, however, it is more convenient to specify all array elements or field values at once. The llvm_array_value and llvm_struct_value functions construct compound values from lists of element values.
llvm_array_value : [SetupValue] -> SetupValuellvm_struct_value : [SetupValue] -> SetupValue
To specify an array or struct in which each element or field is symbolic, it would be possible, but tedious, to use a large combination of llvm_fresh_var and llvm_elem or llvm_field commands. However, the following function can simplify the common case where you want every element or field to have a fresh value.
llvm_fresh_expanded_val : LLVMType -> LLVMSetup SetupValue
The llvm_struct_value function normally creates a struct whose layout obeys the alignment rules of the platform specified in the LLVM file being analyzed. Structs in LLVM can explicitly be “packed”, however, so that every field immediately follows the previous in memory. The following command will create values of such types:
llvm_packed_struct_value : [SetupValue] -> SetupValue
C programs will sometimes make use of pointer casting to implement various kinds of polymorphic behaviors, either via direct pointer casts, or by using union types to codify the pattern. To reason about such cases, the following operation is useful.
llvm_cast_pointer : SetupValue -> LLVMType -> SetupValue
This function function casts the type of the input value (which must be a pointer) so that it points to values of the given type. This mainly affects the results of subsequent llvm_field and llvm_elem calls, and any eventual points_to statements that the resulting pointer flows into. This is especially useful for dealing with C union types, as the type information provided by LLVM is imprecise in these cases.
We can automate the process of applying pointer casts if we have debug information avaliable:
llvm_union : SetupValue -> String -> SetupValue
Given a pointer setup value, this attempts to select the named union branch and cast the type of the pointer. For this to work, debug symbols must be included; moreover, the process of correlating LLVM type information with information contained in debug symbols is a bit heuristic. If llvm_union cannot figure out how to cast a pointer, one can fall back on the more manual llvm_cast_pointer instead.
In the experimental Java verification implementation, the following functions can be used to state the equivalent of a combination of llvm_points_to and either llvm_elem or llvm_field.
jvm_elem_is : JVMValue -> Int -> JVMValue -> JVMSetup ()specifies the value of an array element.jvm_field_is : JVMValue -> String -> JVMValue -> JVMSetup ()specifies the name of an object field.
In the experimental MIR verification implementation, the following functions construct compound values:
mir_array_value : MIRType -> [MIRValue] -> MIRValueconstructs an array of the given type whose elements consist of the given values. Supplying the element type is necessary to support length-0 arrays.mir_enum_value : MIRAdt -> String -> [MIRValue] -> MIRValueconstructs an enum using a particular enum variant. TheMIRAdtarguments determines what enum type to create, theStringvalue determines the name of the variant to use, and the[MIRValue]list are the values to use as elements in the variant.See the “Finding MIR algebraic data types” section (as well as the “Enums” subsection) for more information on how to compute a
MIRAdtvalue to pass tomir_enum_value.mir_slice_value : MIRValue -> MIRValue: see the “MIR slices” section below.mir_slice_range_value : MIRValue -> Int -> Int -> MIRValue: see the “MIR slices” section below.mir_struct_value : MIRAdt -> [MIRValue] -> MIRValueconstruct a struct with the given list of values as elements. TheMIRAdtargument determines what struct type to create.See the “Finding MIR algebraic data types” section for more information on how to compute a
MIRAdtvalue to pass tomir_struct_value.mir_tuple_value : [MIRValue] -> MIRValueconstruct a tuple with the given list of values as elements.
To specify a compound value in which each element or field is symbolic, it would be possible, but tedious, to use a large number of mir_fresh_var invocations in conjunction with the commands above. However, the following function can simplify the common case where you want every element or field to have a fresh value:
mir_fresh_expanded_value : String -> MIRType -> MIRSetup MIRValue
The String argument denotes a prefix to use when generating the names of fresh symbolic variables. The MIRType can be any type, with the exception of reference types (or compound types that contain references as elements or fields), which are not currently supported.
MIR slices
Slices are a unique form of compound type that is currently only used during MIR verification. Unlike other forms of compound values, such as arrays, it is not possible to directly construct a slice. Instead, one must take a slice of an existing reference value that points to the thing being sliced. The following commands are used to construct slices:
mir_slice_value : MIRValue -> MIRValue: the SAWScript expressionmir_slice_value baseis equivalent to the Rust expression&base[..], i.e., a slice of the entirety ofbase.basemust be a reference to an array value (&[T; N]or&mut [T; N]), not an array itself. The type ofmir_slice_value basewill be&[T](ifbaseis an immutable reference) or&mut [T](ifbaseis a mutable reference).mir_slice_range_value : MIRValue -> Int -> Int -> MIRValue: the SAWScript expressionmir_slice_range_value base start endis equivalent to the Rust expression&base[start..end], i.e., a slice over a part ofbasewhich ranges fromstarttoend.basemust be a reference to an array value (&[T; N]or&mut [T; N]), not an array itself. The type ofmir_slice_value basewill be&[T](ifbaseis an immutable reference) or&mut [T](ifbaseis a mutable reference).startandendare assumed to be zero-indexed.startmust not exceedend, andendmust not exceed the length of the array thatbasepoints to.
As an example of how to use these functions, consider this Rust function, which accepts an arbitrary slice as an argument:
We can write a specification that passes a slice to the array [1, 2, 3, 4, 5] as an argument to f:
Alternatively, we can write a specification that passes a part of this array over the range [1..3], i.e., ranging from second element to the fourth. Because this is a half-open range, the resulting slice has length 2:
Note that we are passing references of arrays to mir_slice_value and mir_slice_range_value. It would be an error to pass a bare array to these functions, so the following specification would be invalid:
SAW’s support for slices is currently limited:
SAW specifications cannot say anything about
&strslice arguments or return values at present.The
mir_slice_range_valuefunction must accept bareIntarguments to specify the lower and upper bounds of the range. A consequence of this design is that it is not possible to create a slice with a symbolic length.
If either of these limitations prevent you from using SAW, please file an issue on GitHub.
Finding MIR algebraic data types
We collectively refer to MIR structs and enums together as algebraic data types, or ADTs for short. ADTs have identifiers to tell them apart, and a single ADT declaration can give rise to multiple identifiers depending on how the declaration is used. For example:
This program as a single struct declaration S, which is used in the functions f and g. Note that S’s declaration is polymorphic, as it uses type parameters, but the uses of S in f and g are monomorphic, as S’s type parameters are fully instantiated. Each unique, monomorphic instantiation of an ADT gives rise to its own identifier. In the example above, this might mean that the following identifiers are created when this code is compiled with mir-json:
S<u8, u16>gives rise toexample/abcd123::S::_adt456S<u32, u64>gives rise toexample/abcd123::S::_adt789
The suffix _adt<number> is autogenerated by mir-json and is typically difficult for humans to guess. For this reason, we offer a command to look up an ADT more easily:
mir_find_adt : MIRModule -> String -> [MIRType] -> MIRAdtconsults the givenMIRModuleto find an algebraic data type (MIRAdt). It uses the givenStringas an identifier and the given MIRTypes as the types to instantiate the type parameters of the ADT. If such aMIRAdtcannot be found in theMIRModule, this will raise an error.
Note that the String argument to mir_find_adt does not need to include the _adt<num> suffix, as mir_find_adt will discover this for you. The String is expected to adhere to the identifier conventions described in the “Running a MIR-based verification” section. For instance, the following two lines will look up S<u8, u16> and S<u32, u64> from the example above as MIRAdts:
The mir_adt command (for constructing a struct type), mir_struct_value (for constructing a struct value), and mir_enum_value (for constructing an enum value) commands in turn take a MIRAdt as an argument.
Enums
In addition to taking a MIRAdt as an argument, mir_enum_value also takes a String representing the name of the variant to construct. The variant name should be a short name such as "None" or "Some", and not a full identifier such as "core::option::Option::None" or "core::option::Option::Some". This is because the MIRAdt already contains the full identifiers for all of an enum’s variants, so SAW will use this information to look up a variant’s identifier from a short name. Here is an example of using mir_enum_value in practice:
Note that mir_enum_value can only be used to construct a specific variant. If you need to construct a symbolic enum value that can range over many potential variants, use mir_fresh_expanded_value instead.
Lifetimes
Rust ADTs can have both type parameters as well as lifetime parameters. The following Rust code declares a lifetime parameter 'a on the struct S, as well on the function f that computes an S value:
When mir-json compiles a piece of Rust code that contains lifetime parameters, it will instantiate all of the lifetime parameters with a placeholder MIR type that is simply called lifetime. This is important to keep in mind when looking up ADTs with mir_find_adt, as you will also need to indicate to SAW that the lifetime parameter is instantiated with lifetime. In order to do so, use mir_lifetime. For example, here is how to look up S with 'a instantiated to lifetime:
Note that this part of SAW’s design is subject to change in the future. Ideally, users would not have to care about lifetimes at all at the MIR level; see this issue for further discussion on this point. If that issue is fixed, then we will likely remove mir_lifetime, as it will no longer be necessary.
Bitfields
SAW has experimental support for specifying structs with bitfields, such as in the following example:
Normally, a struct with two uint8_t fields would have an overall size of two bytes. However, because the x and y fields are declared with bitfield syntax, they are instead packed together into a single byte.
Because bitfields have somewhat unusual memory representations in LLVM, some special care is required to write SAW specifications involving bitfields. For this reason, there is a dedicated llvm_points_to_bitfield function for this purpose:
llvm_points_to_bitfield : SetupValue -> String -> SetupValue -> LLVMSetup ()
The type of llvm_points_to_bitfield is similar that of llvm_points_to, except that it takes the name of a field within a bitfield as an additional argument. For example, here is how to assert that the y field in the struct example above should be 0:
Note that the type of the right-hand side value (0, in this example) must be a bitvector whose length is equal to the size of the field within the bitfield. In this example, the y field was declared as y:1, so y’s value must be of type [1].
Note that the following specification is not equivalent to the one above:
llvm_points_to works quite differently from llvm_points_to_bitfield under the hood, so using llvm_points_to on bitfields will almost certainly not work as expected.
In order to use llvm_points_to_bitfield, one must also use the enable_lax_loads_and_stores command:
enable_lax_loads_and_stores: TopLevel ()
Both llvm_points_to_bitfield and enable_lax_loads_and_stores are experimental commands, so these also require using enable_experimental before they can be used.
The enable_lax_loads_and_stores command relaxes some of SAW’s assumptions about uninitialized memory, which is necessary to make llvm_points_to_bitfield work under the hood. For example, reading from uninitialized memory normally results in an error in SAW, but with enable_lax_loads_and_stores, such a read will instead return a symbolic value. At present, enable_lax_loads_and_stores only works with What4-based tactics (e.g., w4_unint_z3); using it with SBV-based tactics (e.g., sbv_unint_z3) will result in an error.
Note that SAW relies on LLVM debug metadata in order to determine which struct fields reside within a bitfield. As a result, you must pass -g to clang when compiling code involving bitfields in order for SAW to be able to reason about them.
Global variables
SAW supports verifying LLVM and MIR specifications involving global variables.
LLVM global variables
Mutable global variables that are accessed in a function must first be allocated by calling llvm_alloc_global on the name of the global.
llvm_alloc_global : String -> LLVMSetup ()
This ensures that all global variables that might influence the function are accounted for explicitly in the specification: if llvm_alloc_global is used in the precondition, there must be a corresponding llvm_points_to in the postcondition describing the new state of that global. Otherwise, a specification might not fully capture the behavior of the function, potentially leading to unsoundness in the presence of compositional verification. (For more details on this point, see the Compositional Verification and Mutable Global Variables section.)
Immutable (i.e. const) global variables are allocated implicitly, and do not require a call to llvm_alloc_global.
Pointers to global variables or functions can be accessed with llvm_global:
llvm_global : String -> SetupValue
Like the pointers returned by llvm_alloc, however, these aren’t initialized at the beginning of symbolic – setting global variables may be unsound in the presence of compositional verification.
To understand the issues surrounding global variables, consider the following C code:
One might initially write the following specifications for f and g:
If globals were always initialized at the beginning of verification, both of these specs would be provable. However, the results wouldn’t truly be compositional. For instance, it’s not the case that f(g(z)) == z + 3 for all z, because both f and g modify the global variable x in a way that crosses function boundaries.
To deal with this, we can use the following function:
llvm_global_initializer : String -> SetupValuereturns the value of the constant global initializer for the named global variable.
Given this function, the specifications for f and g can make this reliance on the initial value of x explicit:
which initializes x to whatever it is initialized to in the C code at the beginning of verification. This specification is now safe for compositional verification: SAW won’t use the specification f_spec unless it can determine that x still has its initial value at the point of a call to f. This specification also constrains y to prevent signed integer overflow resulting from the x + y expression in f, which is undefined behavior in C.
MIR static items
Rust’s static items are the MIR version of global variables. A reference to a static item can be accessed with the mir_static function. This function takes a String representing a static item’s identifier, and this identifier is expected to adhere to the naming conventions outlined in the “Running a MIR-based verification” section:
mir_static : String -> MIRValue
References to static values can be initialized with the mir_points_to command, just like with other forms of references. Immutable static items (e.g., static X: u8 = 42) are initialized implicitly in every SAW specification, so there is no need for users to do so manually. Mutable static items (e.g., static mut Y: u8 = 27), on the other hand, are not initialized implicitly, and users must explicitly pick a value to initialize them with.
The mir_static_initializer function can be used to access the initial value of a static item in a MIR program. Like with mir_static, the String supplied as an argument must be a valid identifier:
mir_static_initializer : String -> MIRValue.
As an example of how to use these functions, here is a Rust program involving static items:
We can write a specification for f like so:
In order to use a specification involving mutable static items for compositional verification, it is required to specify the value of all mutable static items using the mir_points_to command in the specification’s postconditions. For more details on this point, see the Compositional Verification and Mutable Global Variables section.
Preconditions and Postconditions
Sometimes a function is only well-defined under certain conditions, or sometimes you may be interested in certain initial conditions that give rise to specific final conditions. For these cases, you can specify an arbitrary predicate as a precondition or post-condition, using any values in scope at the time.
llvm_precond : Term -> LLVMSetup ()llvm_postcond : Term -> LLVMSetup ()llvm_assert : Term -> LLVMSetup ()jvm_precond : Term -> JVMSetup ()jvm_postcond : Term -> JVMSetup ()jvm_assert : Term -> JVMSetup ()mir_precond : Term -> MIRSetup ()mir_postcond : Term -> MIRSetup ()mir_assert : Term -> MIRSetup ()
These commands take Term arguments, and therefore cannot describe the values of pointers. The “assert” variants will work in either pre- or post-conditions, and are useful when defining helper functions that, e.g., state datastructure invariants that make sense in both phases. The llvm_equal command states that two SetupValues should be equal, and can be used in either the initial or the final state.
llvm_equal : SetupValue -> SetupValue -> LLVMSetup ()
The use of llvm_equal can also sometimes lead to more efficient symbolic execution when the predicate of interest is an equality.
Assuming specifications
Normally, a MethodSpec is the result of both simulation and proof of the target code. However, in some cases, it can be useful to use a MethodSpec to specify some code that either doesn’t exist or is hard to prove. The previously-mentioned assume_unsat tactic omits proof but does not prevent simulation of the function. To skip simulation altogether, one can use one of the following commands:
A Heap-Based Example
To tie all of the command descriptions from the previous sections together, consider the case of verifying the correctness of a C program that computes the dot product of two vectors, where the length and value of each vector are encapsulated together in a struct.
The dot product can be concisely specified in Cryptol as follows:
To implement this in C, let’s first consider the type of vectors:
This struct contains a pointer to an array of 32-bit elements, and a 32-bit value indicating how many elements that array has.
We can compute the dot product of two of these vectors with the following C code (which uses the size of the shorter vector if they differ in size).
The entirety of this implementation can be found in the examples/llvm/dotprod_struct.c file in the saw-script repository.
To verify this program in SAW, it will be convenient to define a couple of utility functions (which are generally useful for many heap-manipulating programs). First, combining allocation and initialization to a specific value can make many scripts more concise:
This creates a pointer p pointing to enough space to store type ty, and then indicates that the pointer points to value v (which should be of that same type).
A common case for allocation and initialization together is when the initial value should be entirely symbolic.
This function returns the pointer just allocated along with the fresh symbolic value it points to.
Given these two utility functions, the dotprod_struct function can be specified as follows:
Any instantiation of this specification is for a specific vector length n, and assumes that both input vectors have that length. That length n automatically becomes a type variable in the subsequent Cryptol expressions, and the backtick operator is used to reify that type as a bit vector of length 32.
The entire script can be found in the dotprod_struct-crucible.saw file alongside dotprod_struct.c.
Running this script results in the following:
Using Ghost State
In some cases, information relevant to verification is not directly present in the concrete state of the program being verified. This can happen for at least two reasons:
When providing specifications for external functions, for which source code is not present. The external code may read and write global state that is not directly accessible from the code being verified.
When the abstract specification of the program naturally uses a different representation for some data than the concrete implementation in the code being verified does.
One solution to these problems is the use of ghost state. This can be thought of as additional global state that is visible only to the verifier. Ghost state with a given name can be declared at the top level with the following function:
declare_ghost_state : String -> TopLevel Ghost
Ghost state variables do not initially have any particluar type, and can store data of any type. Given an existing ghost variable the following functions can be used to specify its value:
llvm_ghost_value : Ghost -> Term -> LLVMSetup ()jvm_ghost_value : Ghost -> Term -> JVMSetup ()mir_ghost_value : Ghost -> Term -> MIRSetup ()
These can be used in either the pre state or the post state, to specify the value of ghost state either before or after the execution of the function, respectively.
An Extended Example
To tie together many of the concepts in this manual, we now present a non-trivial verification task in its entirety. All of the code for this example can be found in the examples/salsa20 directory of the SAWScript repository.
Salsa20 Overview
Salsa20 is a stream cipher developed in 2005 by Daniel J. Bernstein, built on a pseudorandom function utilizing add-rotate-XOR (ARX) operations on 32-bit words4. Bernstein himself has provided several public domain implementations of the cipher, optimized for common machine architectures. For the mathematically inclined, his specification for the cipher can be found here.
The repository referenced above contains three implementations of the Salsa20 cipher: A reference Cryptol implementation (which we take as correct in this example), and two C implementations, one of which is from Bernstein himself. For this example, we focus on the second of these C implementations, which more closely matches the Cryptol implementation. Full verification of Bernstein’s implementation is available in examples/salsa20/djb, for the interested. The code for this verification task can be found in the files named according to the pattern examples/salsa20/(s|S)alsa20.*.
Specifications
We now take on the actual verification task. This will be done in two stages: We first define some useful utility functions for constructing common patterns in the specifications for this type of program (i.e. one where the arguments to functions are modified in-place.) We then demonstrate how one might construct a specification for each of the functions in the Salsa20 implementation described above.
Utility Functions
We first define the function alloc_init : LLVMType -> Term -> LLVMSetup SetupValue.
alloc_init ty v returns a SetupValue consisting of a pointer to memory allocated and initialized to a value v of type ty. alloc_init_readonly does the same, except the memory allocated cannot be written to.
We now define ptr_to_fresh : String -> LLVMType -> LLVMSetup (Term, SetupValue).
ptr_to_fresh n ty returns a pair (x, p) consisting of a fresh symbolic variable x of type ty and a pointer p to it. n specifies the name that SAW should use when printing x. ptr_to_fresh_readonly does the same, but returns a pointer to space that cannot be written to.
Finally, we define oneptr_update_func : String -> LLVMType -> Term -> LLVMSetup ().
oneptr_update_func n ty f specifies the behavior of a function that takes a single pointer (with a printable name given by n) to memory containing a value of type ty and mutates the contents of that memory. The specification asserts that the contents of this memory after execution are equal to the value given by the application of f to the value in that memory before execution.
The quarterround operation
The C function we wish to verify has type void s20_quarterround(uint32_t *y0, uint32_t *y1, uint32_t *y2, uint32_t *y3).
The function’s specification generates four symbolic variables and pointers to them in the precondition/setup stage. The pointers are passed to the function during symbolic execution via llvm_execute_func. Finally, in the postcondition/return stage, the expected values are computed using the trusted Cryptol implementation and it is asserted that the pointers do in fact point to these expected values.
Simple Updating Functions
The following functions can all have their specifications given by the utility function oneptr_update_func implemented above, so there isn’t much to say about them.
32-Bit Key Expansion
The next function of substantial behavior that we wish to verify has the following prototype:
This function’s specification follows a similar pattern to that of s20_quarterround, though for extra assurance we can make sure that the function does not write to the memory pointed to by k or n using the utility ptr_to_fresh_readonly, as this function should only modify keystream. Besides this, we see the call to the trusted Cryptol implementation specialized to a=2, which does 32-bit key expansion (since the Cryptol implementation can also specialize to a=1 for 16-bit keys). This specification can easily be changed to work with 16-bit keys.
32-bit Key Encryption
Finally, we write a specification for the encryption function itself, which has type
As before, we can ensure this function does not modify the memory pointed to by key or nonce. We take si, the stream index, to be 0. The specification is parameterized on a number n, which corresponds to buflen. Finally, to deal with the fact that this function returns a status code, we simply specify that we expect a success (status code 0) as the return value in the postcondition stage of the specification.
Verifying Everything
Finally, we can verify all of the functions. Notice the use of compositional verification and that path satisfiability checking is enabled for those functions with loops not bounded by explicit constants. Notice that we prove the top-level function for several sizes; this is due to the limitation that SAW can only operate on finite programs (while Salsa20 can operate on any input size.)
Verifying Cryptol FFI functions
SAW has special support for verifying the correctness of Cryptol’s foreign functions, implemented in a language such as C which compiles to LLVM, provided that there exists a reference Cryptol implementation of the function as well. Since the way in which foreign functions are called is precisely specified by the Cryptol FFI, SAW is able to generate a LLVMSetup () spec directly from the type of a Cryptol foreign function. This is done with the llvm_ffi_setup command, which is experimental and requires enable_experimental; to be run beforehand.
For instance, for the simple imported Cryptol foreign function foreign add : [32] -> [32] -> [32] we can obtain a LLVMSetup spec simply by writing
which behind the scenes expands to something like
Polymorphism
In general, Cryptol foreign functions can be polymorphic, with type parameters of kind #, representing e.g. the sizes of input sequences. However, any individual LLVMSetup () spec only specifies the behavior of the LLVM function on inputs of concrete sizes. We handle this by allowing the argument term of llvm_ffi_setup to contain any necessary type arguments in addition to the Cryptol function name, so that the resulting term is monomorphic. The user can then define a parameterized specification simply as a SAWScript function in the usual way. For example, for a function foreign f : {n, m} (fin n, fin m) => [n][32] -> [m][32], we can obtain a parameterized LLVMSetup spec by
Note that the Term parameter that llvm_ffi_setup takes is restricted syntactically to the format described above ({{ fun`{tyArg0, tyArg1, ..., tyArgN} }}), and cannot be any arbitrary term.
Supported types
llvm_ffi_setup supports all Cryptol types that are supported by the Cryptol FFI, with the exception of Integer, Rational, Z, and Float. Integer, Rational, and Z are not supported since they are translated to gmp arbitrary-precision types which are hard for SAW to handle without additional overrides. There is no fundamental obstacle to supporting Float, and in fact llvm_ffi_setup itself does work with Cryptol floating point types, but the underlying functions such as llvm_fresh_var do not, so until that is implemented llvm_ffi_setup can generate a spec involving floating point types but it cannot actually be run.
Performing the verification
The resulting LLVMSetup () spec can be used with the existing llvm_verify function to perform the actual verification. And the LLVMSpec output from that can be used as an override as usual for further compositional verification.
As with the Cryptol FFI itself, SAW does not manage the compilation of the C source implementations of foreign functions to LLVM bitcode. For the verification to be meaningful, is expected that the LLVM module passed to llvm_verify matches the compiled dynamic library actually used with the Cryptol interpreter. Alternatively, on x86_64 Linux, SAW can perform verification directly on the .so ELF file with the experimental llvm_verify_x86 command.
Extraction to the Coq theorem prover
In addition to the (semi-)automatic and compositional proof modes already discussed above, SAW has experimental support for exporting Cryptol and SAWCore values as terms to the Coq proof assistant5. This is intended to support more manual proof efforts for properties that go beyond what SAW can support (for example, proofs requiring induction) or for connecting to preexisting formalizations in Coq of useful algorithms (e.g. the fiat crypto library6).
This support consists of two related pieces. The first piece is a library of formalizations of the primitives underlying Cryptol and SAWCore and various supporting concepts that help bridge the conceptual gap between SAW and Coq. The second piece is a term translation that maps the syntactic forms of SAWCore onto corresponding concepts in Coq syntax, designed to dovetail with the concepts defined in the support library. SAWCore is a quite similar language to the core calculus underlying Coq, so much of this translation is quite straightforward; however, the languages are not exactly equivalent, and there are some tricky cases that mostly arise from Cryptol code that can only be partially supported. We will note these restrictions later in the manual.
We expect this extraction process to work with a fairly wide range of Coq versions, as we are not using bleeding-edge Coq features. It has been most fully tested with Coq version 8.13.2.
Support Library
In order to make use of SAW’s extraction capabilities, one must first compile the support library using Coq so that the included definitions and theorems can be referenced by the extracted code. From the top directory of the SAW source tree, the source code for this support library can be found in the saw-core-coq/coq subdirectory. In this subdirectory you will find a _CoqProject and a Makefile. A simple make invocation should be enough to compile all the necessary files, assuming Coq is installed and coqc is available in the user’s PATH. HTML documentation for the support library can also be generated by make html from the same directory.
Once the library is compiled, the recommended way to import it into your subsequent development is by adding the following lines to your _CoqProject file:
Here <SAWDIR> refers to the location on your system where the SAWScript source tree is checked out. This will add the relevant library files to the CryptolToCoq namespace, where the extraction process will expect to find them.
The support library for extraction is broken into two parts: those files which are handwritten, versus those that are automatically generated. The handwritten files are generally fairly readable and are reasonable for human inspection; they define most of the interesting pipe-fitting that allows Cryptol and SAWCore definitions to connect to corresponding Coq concepts. In particular the file SAWCoreScaffolding.v file defines most of the bindings of base types to Coq types, and the SAWCoreVectorsAsCoqVectors.v defines the core bitvector operations. The automatically generated files are direct translations of the SAWCore source files (saw-core/prelude/Prelude.sawcore and cryptol-saw-core/saw/Cryptol.sawcore) that correspond to the standard libraries for SAWCore and Cryptol, respectively.
The autogenerated files are intended to be kept up-to-date with changes in the corresponding sawcore files, and end users should not need to generate them. Nonetheless, if they are out of sync for some reason, these files may be regenerated using the saw executable by running (cd saw-core-coq; saw saw/generate_scaffolding.saw) from the top-level of the SAW source directory before compiling them with Coq as described above.
You may also note some additional files and concepts in the standard library, such as CompM.v, and a variety of lemmas and definitions related to it. These definitions are related to the “heapster” system, which form a separate use-case for the SAWCore to Coq translation. These definitions will not be used for code extracted from Cryptol.
Cryptol module extraction
There are several modes of use for the SAW to Coq term extraction facility, but the easiest to use is whole Cryptol module extraction. This will extract all the definitions in the given Cryptol module, together with it’s transitive dependencies, into a single Coq module which can then be compiled and pulled into subsequence developments.
Suppose we have a Cryptol source file named source.cry and we want to generate a Coq file named output.v. We can accomplish this by running the following commands in saw (either directly from the saw command prompt, or via a script file)
In this default mode, identifiers in the Cryptol source will be directly translated into identifiers in Coq. This may occasionally cause problems if source identifiers clash with Coq keywords or preexisting definitions. The third argument to write_coq_cryptol_module can be used to remap such names if necessary by giving a list of (in,out) pairs of names. The fourth argument is a list of source identifiers to skip translating, if desired. Authoritative online documentation for this command can be obtained directly from the saw executable via :help write_coq_cryptol_module after enable_experimental.
The resulting “output.v” file will have some of the usual hallmarks of computer-generated code; it will have poor formatting and, explicit parenthesis and fully-qualified names. Thankfully, once loaded into Coq, the Coq pretty-printer will do a much better job of rendering these terms in a somewhat human-readable way.
Proofs involving uninterpreted functions
It is possible to write a Cryptol module that references uninterpreted functions by using the primitive keyword to declare them in your Cryptol source. Primitive Cryptol declarations will be translated into Coq section variables; as usual in Coq, uses of these section variables will be translated into additional parameters to the definitions from outside the section. In this way, consumers of the translated module can instantiate the declared Cryptol functions with corresponding terms in subsequent Coq developments.
Although the Cryptol interpreter itself will not be able to compute with declared but undefined functions of this sort, they can be used both to provide specifications for functions to be verified with llvm_verify or jvm_verify and also for Coq extraction.
For example, if I write the following Cryptol source file:
After extraction, the generated term g will have Coq type:
Translation limitations and caveats
Translation from Cryptol to Coq has a number of fundamental limitations that must be addressed. The most severe of these is that Cryptol is a fully general-recursive language, and may exhibit runtime errors directly via calls to the error primitive, or via partial operations (such as indexing a sequence out-of-bounds). The internal language of Coq, by contrast, is a strongly-normalizing language of total functions. As such, our translation is unable to extract all Cryptol programs.
Recursive programs
The most severe of the currently limitations for our system is that the translation is unable to translate any recursive Cryptol program. Doing this would require us to attempt to find some termination argument for the recursive function sufficient to satisfy Coq; for now, no attempt is made to do so. if you attempt to extract a recursive function, SAW will produce an error about a “malformed term” with Prelude.fix as the head symbol.
Certain limited kinds of recursion are available via the foldl Cryptol primitive operation, which is translated directly into a fold operation in Coq. This is sufficient for many basic iterative algorithms.
Type coercions
Another limitation of the translation system is that Cryptol uses SMT solvers during its typechecking process and uses the results of solver proofs to justify some of its typing judgments. When extracting these terms to Coq, type equality coercions must be generated. Currently, we do not have a good way to transport the reasoning done inside Cryptol’s typechecker into Coq, so we just supply a very simple Ltac tactic to discharge these coercions (see solveUnsafeAssert in CryptolPrimitivesForSAWCoreExtra.v). This tactic is able to discover simple coercions, but for anything nontrivial it may fail. The result will be a typechecking failure when compiling the generated code in Coq when the tactic fails. If you encounter this problem, it may be possible to enhance the solveUnsafeAssert tactic to cover your use case.
Error terms
A final caveat that is worth mentioning is that Cryptol can sometimes produce runtime errors. These can arise from explicit calls to the error primitive, or from partially defined operations (e.g., division by zero or sequence access out of bounds). Such instances are translated to occurrences of an unrealized Coq axiom named error. In order to avoid introducing an inconsistent environment, the error axiom is restricted to apply only to inhabited types. All the types arising from Cryptol programs are inhabited, so this is no problem in principle. However, collecting and passing this information around on the Coq side is a little tricky.
The main technical device we use here is the Inhabited type class; it simply asserts that a type has some distinguished inhabitant. We provide instances for the base types and type constructors arising from Cryptol, so the necessary instances ought to be automatically constructed when needed. However, polymorphic Cryptol functions add some complications, as type arguments must also come together with evidence that they are inhabited. The translation process takes care to add the necessary Inhabited arguments, so everything ought to work out. However, if Coq typechecking of generated code fails with errors about Inhabited class instances, it likely represents some problem with this aspect of the translation.
Analyzing Hardware Circuits using Yosys
SAW has experimental support for analysis of hardware descriptions written in VHDL (via GHDL) through an intermediate representation produced by Yosys. This generally follows the same conventions and idioms used in the rest of SAWScript.
Processing VHDL With Yosys
Given a VHDL file test.vhd containing an entity test, one can generate an intermediate representation test.json suitable for loading into SAW:
It can sometimes be helpful to invoke additional Yosys passes between the ghdl and write_json commands. For example, at present SAW does not support the $pmux cell type. Yosys is able to convert $pmux cells into trees of $mux cells using the pmuxtree command. We expect there are many other situations where Yosys’ considerable library of commands is valuable for pre-processing.
Example: Ripple-Carry Adder
Consider three VHDL entities. First, a half-adder:
Next, a one-bit adder built atop that half-adder:
Finally, a four-bit adder:
Using GHDL and Yosys, we can convert the VHDL source above into a format that SAW can import. If all of the code above is in a file adder.vhd, we can run the following commands:
The produced file adder.json can then be loaded into SAW with yosys_import:
yosys_import returns a Term with a Cryptol record type, where the fields correspond to each VHDL module. We can access the fields of this record like we would any Cryptol record, and call the functions within like any Cryptol function.
We can also use all of SAW’s infrastructure for asking solvers about Terms, such as the sat and prove commands. For example:
The full library of ProofScript tactics is available in this setting. If necessary, proof tactics like simplify can be used to rewrite goals before querying a solver.
Special support is provided for the common case of equivalence proofs between HDL modules and other Terms (e.g. Cryptol functions, other HDL modules, or “extracted” imperative LLVM or JVM code). The command yosys_verify has an interface similar to llvm_verify: given a specification, some lemmas, and a proof tactic, it produces evidence of a proven equivalence that may be passed as a lemma to future calls of yosys_verify. For example, consider the following Cryptol specifications for one-bit and four-bit adders:
We can prove equivalence between cryfull and the VHDL full module:
The result full_spec can then be used as an “override” when proving equivalence between cryadd4 and the VHDL add4 module:
The above could also be accomplished through the use of prove_print and term rewriting, but it is much more verbose.
yosys_verify may also be given a list of preconditions under which the equivalence holds. For example, consider the following Cryptol specification for full that ignores the cin bit:
This is not equivalent to full in general, but it is if constrained to inputs where cin = 0. We may express that precondition like so:
The resulting override full_nocarry_spec may still be used in the proof for add4 (this is accomplished by rewriting to a conditional expression).
API Reference
N.B: The following commands must first be enabled using enable_experimental.
yosys_import : String -> TopLevel Termproduces aTermgiven the path to a JSON file produced by the Yosyswrite_jsoncommand. The resulting term is a Cryptol record, where each field corresponds to one HDL module exported by Yosys. Each HDL module is in turn represented by a function from a record of input port values to a record of output port values. For example, consider a Yosys JSON file derived from the following VHDL entities: ~~~~vhdl entity half is port ( a : in std_logic; b : in std_logic; c : out std_logic; s : out std_logic ); end half;entity full is port ( a : in std_logic; b : in std_logic; cin : in std_logic; cout : out std_logic; s : out std_logic ); end full; ~~~~ The resulting
Termwill have the type: ~~~~ { half : {a : [1], b : [1]} -> {c : [1], s : [1]} , full : {a : [1], b : [1], cin : [1]} -> {cout : [1], s : [1]} } ~~~~yosys_verify : Term -> [Term] -> Term -> [YosysTheorem] -> ProofScript () -> TopLevel YosysTheoremproves equality between an HDL module and a specification. The first parameter is the HDL module - given a recordmfromyosys_import, this will typically look something like{{ m.foo }}. The second parameter is a list of preconditions for the equality. The third parameter is the specification, a term of the same type as the HDL module, which will typically be some Cryptol function or another HDL module. The fourth parameter is a list of “overrides”, which witness the results of previousyosys_verifyproofs. These overrides can be used to simplify terms by replacing use sites of submodules with their specifications.Note that
Terms derived from HDL modules are “first class”, and are not restricted toyosys_verify: they may also be used with SAW’s typicalTerminfrastructure likesat,prove_print, term rewriting, etc.yosys_verifysimply provides a convenient and familiar interface, similar tollvm_verifyorjvm_verify.
Bisimulation Prover
SAW contains a bisimulation prover to prove that two terms simulate each other. This prover allows users to prove that two terms executing in lockstep satisfy some relations over the state of each circuit and their outputs. This type of proof is useful in demonstrating the eventual equivalence of two circuits, or of a circuit and a functional specification. SAW enables these proofs with the experimental prove_bisim command:
When invoking prove_bisim strat theorems srel orel lhs rhs, the arguments represent the following:
strat: A proof strategy to use during verification.theorems: A list of already proven bisimulation theorems.srel: A state relation betweenlhsandrhs. This relation must have the typelhsState -> rhsState -> Bit. The relation’s first argument islhs’s state prior to execution. The relation’s second argument isrhs’s state prior to execution.srelthen returns aBitindicating whether the two arguments satisfy the bisimulation’s state relation.orel: An output relation betweenlhsandrhs. This relation must have the type(lhsState, output) -> (rhsState, output) -> Bit. The relation’s first argument is a pair consisting oflhs’s state and output following execution. The relation’s second argument is a pair consisting ofrhs’s state and output following execution.orelthen returns aBitindicating whether the two arguments satisfy the bisimulation’s output relation.lhs: A term that simulatesrhs.lhsmust have the type(lhsState, input) -> (lhsState, output). The first argument tolhsis a tuple containing the internal state oflhs, as well as the input tolhs.lhsreturns a tuple containing its internal state after execution, as well as its output.rhs: A term that simulateslhs.rhsmust have the type(rhsState, input) -> (rhsState, output). The first argument torhsis a tuple containing the internal state ofrhs, as well as the input torhs.rhsreturns a tuple containing its internal state after execution, as well as its output.
On success, prove_bisim returns a BisimTheorem that can be used in future bisimulation proofs to enable compositional bisimulation proofs. On failure, prove_bisim will abort.
Bisimulation Example
This section walks through an example proving that the Cryptol implementation of an AND gate that makes use of internal state and takes two cycles to complete is equivalent to a pure function that computes the logical AND of its inputs in one cycle. First, we define the implementation’s state type:
andState is a record type with three fields:
loaded: ABitindicating whether the input to the AND gate has been loaded into the state record.origX: ABitstoring the first input to the AND gate.origY: ABitstoring the second input to the AND gate.
Now, we define the AND gate’s implementation:
andImp takes a tuple as input where the first field is an andState holding the gate’s internal state, and second field is a tuple containing the inputs to the AND gate. andImp returns a tuple consisting of the updated andState and the gate’s output. The output is a tuple where the first field is a ready bit that is 1 when the second field is ready to be read, and the second field is the result of gate’s computation.
andImp takes two cycles to complete:
The first cycle loads the inputs into its state’s
origXandorigYfields and setsloadedtoTrue. It sets both of its output bits to0.The second cycle uses the stored input values to compute the logical AND. It sets its ready bit to
1and its second output to the logical AND result.
So long as the inputs remain fixed after the second cycle, andImp’s output remains unchanged. If the inputs change, then andImp restarts the computation (even if the inputs change between the first and second cycles).
Next, we define the pure function we’d like to prove andImp bisimilar to:
andSpec takes a tuple as input where the first field is (), indicating that andSpec is a pure function without internal state, and the second field is a tuple containing the inputs to the AND function. andSpec returns a tuple consisting of () (again, because andSpec is stateless) and the function’s output. Like andImp, the output is a tuple where the first field is a ready bit that is 1 when the second field is ready to be read, and the second field is the result of the function’s computation.
andSpec completes in a single cycle, and as such its ready bit is always 1. It computes the logical AND directly on the function’s inputs using Cryptol’s (&&) operator.
Next, we define a state relation over andImp and andSpec:
andStateRel takes two arguments:
An
andStateforandImp.An empty state (
()) forandSpec.
andStateRel returns a Bit indicating whether the relation is satisified. In this case, andStateRel always returns True because andSpec is stateless and therefore the state relation permits andImp to accept any state.
Lastly, we define a relation over andImp and andSpec:
andOutputRel takes two arguments:
A return value from
andImp. Specifically, a pair consisting of anandStateand a pair containing a ready bit and result of the logical AND.A return value from
andSpec. Specifically, a pair consisting of an empty state()and a pair containing a ready bit and result of the logical AND.
andOutputRel returns a Bit indicating whether the relation is satisfied. It considers the relation satisfied in two ways:
If
andImp’s ready bit is set, the relation is satisfied if the output valuesimpOandspecOfromandImpandandSpecrespectively are equivalent.If
andImp’s ready bit is not set, the relation is satisfied.
Put another way, the relation is satisfied if the end result of andImp and andSpec are equivalent. The relation permits intermediate outputs to differ.
We can verify that this relation is always satisfied–and therefore the two terms are bisimilar–by using prove_bisim:
Upon running this script, SAW prints:
Building a NAND gate
We can make the example more interesting by reusing components to build a NAND gate. We first define a state type for the NAND gate implementation that contains andImp’s state. This NAND gate will not need any additional state, so we will define a type nandState that is equal to andState:
Now, we define an implementation nandImp that calls andImp and negates the result:
Note that nandImp is careful to preserve the ready status of andImp. Because nandImp relies on andImp, it also takes two cycles to compute the logical NAND of its inputs.
Next, we define a specification nandSpec in terms of andSpec:
As with andSpec, nandSpec is pure and computes its result in a single cycle.
Next, we define a state relation over nandImp and nandSpec:
As with andStateRel, this state relation is always True because nandSpec is stateless.
Lastly, we define an output relation indicating that nandImp and nandSpec produce equivalent results once nandImp’s ready bit is 1:
To prove that nandImp and nandSpec are bisimilar, we again use prove_bisim. This time however, we can reuse the bisimulation proof for the AND gate by including it in the theorems paramter for prove_bisim:
Upon running this script, SAW prints:
Understanding the proof goals
While not necessary for simple proofs, more advanced proofs may require inspecting proof goals. prove_bisim generates and attempts to solve the following proof goals:
where the variables in the foralls are:
s1: Initial state forlhss2: Initial state forrhsin: Input value tolhsandrhsout1: Initial output value forlhsout2: Initial output value forrhs
The STATE RELATION THEOREM verifies that the output relation properly captures the guarantees of the state relation. The OUTPUT RELATION THEOREM verifies that if lhs and rhs are executed with related states, then the result of that execution is also related. These two theorems together guarantee that the terms simulate each other.
When using composition, prove_bisim also generates and attempts to solve the proof goal below for any successfully applied BisimTheorem in the theorems list:
where g_lhs is an outer term containing a call to an inner term f_lhs represented by a BisimTheorem and g_rhs is an outer term containing a call to an inner term f_rhs represented by the same BisimTheorem. The variables in COMPOSITION SIDE CONDITION are:
extract_inner_state x x_s y: A helper function that takes an outer termx, an outer statex_s, and an inner termy, and returns the inner state ofx_sthatxpasses toy.g_lhs_s: The state forg_lhsg_rhs_s: The state forg_rhsg_srel: The state relation forg_lhsandg_rhsf_srel: The state relation forf_lhsandf_rhsf_lhs_s: The state forf_lhs, as represented ing_lhs_s(extracted usingextract_inner_state).f_rhs_s: The state forf_rhs, as represented ing_rhs_s(extracted usingextract_inner_state).
The COMPOSITION SIDE CONDITION exists to verify that the terms in the bisimulation relation properly set up valid states for subterms they contain.
Limitiations
For now, the prove_bisim command has a couple limitations:
lhsandrhsmust be named functions. This is becauseprove_bisimuses these names to perform substitution when making use of compositionality.Each subterm present in the list of bisimulation theorems already proven may be invoked at most once in
lhsorrhs. That is, if some functiong_lhscallsf_lhs, andprove_bisimis invoked with aBisimTheoremproving thatf_lhsis bisimilar tof_rhs, theng_lhsmay callf_lhsat most once.
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