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Claripy

angr's solver engine is called Claripy. Claripy exposes the following design:

- Claripy ASTs (the subclasses of claripy.ast.Base) provide a unified way to interact with concrete and symbolic expressions
`Frontend`

s provide different paradigms for evaluating these expressions. For example, the`FullFrontend`

solves expressions using something like an SMT solver backend, while`LightFrontend`

handles them by using an abstract (and approximating) data domain backend.- Each
`Frontend`

needs to, at some point, do actual operation and evaluations on an AST. ASTs don't support this on their own. Instead,`Backend`

s translate ASTs into backend objects (i.e., Python primitives for`BackendConcrete`

, Z3 expressions for`BackendZ3`

, strided intervals for`BackendVSA`

, etc) and handle any appropriate state-tracking objects (such as tracking the solver state in the case of`BackendZ3`

). Roughly speaking, frontends take ASTs as inputs and use backends to`backend.convert()`

those ASTs into backend objects that can be evaluated and otherwise reasoned about. `FrontendMixin`

s customize the operation of`Frontend`

s. For example,`ModelCacheMixin`

caches solutions from an SMT solver.- The combination of a Frontend, a number of FrontendMixins, and a number of Backends comprise a claripy
`Solver`

.

Internally, Claripy seamlessly mediates the co-operation of multiple disparate backends -- concrete bitvectors, VSA constructs, and SAT solvers. It is pretty badass.

Most users of angr will not need to interact directly with Claripy (except for, maybe, claripy AST objects, which represent symbolic expressions) -- angr handles most interactions with Claripy internally. However, for dealing with expressions, an understanding of Claripy might be useful.

Claripy ASTs

Claripy ASTs abstract away the differences between mathematical constructs that Claripy supports. They define a tree of operations (i.e.,

`(a + b) / c)`

on any type of underlying data. Claripy handles the application of these operations on the underlying objects themselves by dispatching requests to the backends.Currently, Claripy supports the following types of ASTs:

Name

Description

Supported By (Claripy Backends)

Example Code

BV

This is a bitvector, whether symbolic (with a name) or concrete (with a value). It has a size (in bits).

BackendConcrete, BackendVSA, BackendZ3

- Create a 32-bit symbolic bitvector "x":
`claripy.BVS('x', 32)`

- Create a 32-bit bitvector with the value
`0xc001b3475`

:`claripy.BVV(0xc001b3a75, 32)`

- Create a 32-bit "strided interval" (see VSA documentation) that can be any divisible-by-10 number between 1000 and 2000:
`claripy.SI(name='x', bits=32, lower_bound=1000, upper_bound=2000, stride=10)`

FP

This is a floating-point number, whether symbolic (with a name) or concrete (with a value).

BackendConcrete, BackendZ3

- Create a
`claripy.fp.FSORT_DOUBLE`

symbolic floating point "b":`claripy.FPS('b', claripy.fp.FSORT_DOUBLE)`

- Create a
`claripy.fp.FSORT_FLOAT`

floating point with value`3.2`

:`claripy.FPV(3.2, claripy.fp.FSORT_FLOAT)`

Bool

This is a boolean operation (True or False).

BackendConcrete, BackendVSA, BackendZ3

`claripy.BoolV(True)`

, or `claripy.true`

or `claripy.false`

, or by comparing two ASTs (i.e., `claripy.BVS('x', 32) < claripy.BVS('y', 32)`

All of the above creation code returns claripy.AST objects, on which operations can then be carried out.

ASTs provide several useful operations.

>>> import claripy

â€‹

>>> bv = claripy.BVV(0x41424344, 32)

â€‹

# Size - you can get the size of an AST with .size()

>>> assert bv.size() == 32

â€‹

# Reversing - .reversed is the reversed version of the BVV

>>> assert bv.reversed is claripy.BVV(0x44434241, 32)

>>> assert bv.reversed.reversed is bv

â€‹

# Depth - you can get the depth of the AST

>>> print(bv.depth)

>>> assert bv.depth == 1

>>> x = claripy.BVS('x', 32)

>>> assert (x+bv).depth == 2

>>> assert ((x+bv)/10).depth == 3

Applying a condition (==, !=, etc) on ASTs will return an AST that represents the condition being carried out. For example:

>>> r = bv == x

>>> assert isinstance(r, claripy.ast.Bool)

â€‹

>>> p = bv == bv

>>> assert isinstance(p, claripy.ast.Bool)

>>> assert p.is_true()

You can combine these conditions in different ways.

>>> q = claripy.And(claripy.Or(bv == x, bv * 2 == x, bv * 3 == x), x == 0)

>>> assert isinstance(p, claripy.ast.Bool)

The usefulness of this will become apparent when we discuss Claripy solvers.

In general, Claripy supports all of the normal Python operations (+, -, |, ==, etc), and provides additional ones via the Claripy instance object. Here's a list of available operations from the latter.

Name

Description

Example

LShR

Logically shifts a bit expression (BVV, BV, SI) to the right.

`claripy.LShR(x, 10)`

SignExt

Sign-extends a bit expression.

`claripy.SignExt(32, x)`

or `x.sign_extend(32)`

ZeroExt

Zero-extends a bit expression.

`claripy.ZeroExt(32, x)`

or `x.zero_extend(32)`

Extract

Extracts the given bits (zero-indexed from the *right*, inclusive) from a bit expression.

Extract the rightmost byte of x:

`claripy.Extract(7, 0, x)`

or `x[7:0]`

Concat

Concatenates several bit expressions together into a new bit expression.

`claripy.Concat(x, y, z)`

RotateLeft

Rotates a bit expression left.

`claripy.RotateLeft(x, 8)`

RotateRight

Rotates a bit expression right.

`claripy.RotateRight(x, 8)`

Reverse

Endian-reverses a bit expression.

`claripy.Reverse(x)`

or `x.reversed`

And

Logical And (on boolean expressions)

`claripy.And(x == y, x > 0)`

Or

Logical Or (on boolean expressions)

`claripy.Or(x == y, y < 10)`

Not

Logical Not (on a boolean expression)

`claripy.Not(x == y)`

is the same as `x != y`

If

An If-then-else

Choose the maximum of two expressions:

`claripy.If(x > y, x, y)`

ULE

Unsigned less than or equal to.

Check if x is less than or equal to y:

`claripy.ULE(x, y)`

ULT

Unsigned less than.

Check if x is less than y:

`claripy.ULT(x, y)`

UGE

Unsigned greater than or equal to.

Check if x is greater than or equal to y:

`claripy.UGE(x, y)`

UGT

Unsigned greater than.

Check if x is greater than y:

`claripy.UGT(x, y)`

SLE

Signed less than or equal to.

Check if x is less than or equal to y:

`claripy.SLE(x, y)`

SLT

Signed less than.

Check if x is less than y:

`claripy.SLT(x, y)`

SGE

Signed greater than or equal to.

Check if x is greater than or equal to y:

`claripy.SGE(x, y)`

SGT

Signed greater than.

Check if x is greater than y:

`claripy.SGT(x, y)`

`>`

, `<`

, `>=`

, and `<=`

are unsigned in Claripy. This is different than their behavior in Z3, because it seems more natural in binary analysis.Solvers

The main point of interaction with Claripy are the Claripy Solvers. Solvers expose an API to interpret ASTs in different ways and return usable values. There are several different solvers.

Name

Description

Solver

This is analogous to a

`z3.Solver()`

. It is a solver that tracks constraints on symbolic variables and uses a constraint solver (currently, Z3) to evaluate symbolic expressions.SolverVSA

This solver uses VSA to reason about values. It is an *approximating* solver, but produces values without performing actual constraint solves.

SolverReplacement

This solver acts as a pass-through to a child solver, allowing the replacement of expressions on-the-fly. It is used as a helper by other solvers and can be used directly to implement exotic analyses.

SolverHybrid

This solver combines the SolverReplacement and the Solver (VSA and Z3) to allow for *approximating* values. You can specify whether or not you want an exact result from your evaluations, and this solver does the rest.

SolverComposite

This solver implements optimizations that solve smaller sets of constraints to speed up constraint solving.

Some examples of solver usage:

# create the solver and an expression

>>> s = claripy.Solver()

>>> x = claripy.BVS('x', 8)

â€‹

# now let's add a constraint on x

>>> s.add(claripy.ULT(x, 5))

â€‹

>>> assert sorted(s.eval(x, 10)) == [0, 1, 2, 3, 4]

>>> assert s.max(x) == 4

>>> assert s.min(x) == 0

â€‹

# we can also get the values of complex expressions

>>> y = claripy.BVV(65, 8)

>>> z = claripy.If(x == 1, x, y)

>>> assert sorted(s.eval(z, 10)) == [1, 65]

â€‹

# and, of course, we can add constraints on complex expressions

>>> s.add(z % 5 != 0)

>>> assert s.eval(z, 10) == (1,)

>>> assert s.eval(x, 10) == (1,) # interestingly enough, since z can't be y, x can only be 1!

Custom solvers can be built by combining a Claripy Frontend (the class that handles the actual interaction with SMT solver or the underlying data domain) and some combination of frontend mixins (that handle things like caching, filtering out duplicate constraints, doing opportunistic simplification, and so on).

Claripy Backends

Backends are Claripy's workhorses. Claripy exposes ASTs to the world, but when actual computation has to be done, it pushes those ASTs into objects that can be handled by the backends themselves. This provides a unified interface to the outside world while allowing Claripy to support different types of computation. For example, BackendConcrete provides computation support for concrete bitvectors and booleans, BackendVSA introduces VSA constructs such as StridedIntervals (and details what happens when operations are performed on them, and BackendZ3 provides support for symbolic variables and constraint solving.

There are a set of functions that a backend is expected to implement. For all of these functions, the "public" version is expected to be able to deal with claripy's AST objects, while the "private" version should only deal with objects specific to the backend itself. This is distinguished with Python idioms: a public function will be named func() while a private function will be _func(). All functions should return objects that are usable by the backend in its private methods. If this can't be done (i.e., some functionality is being attempted that the backend can't handle), the backend should raise a BackendError. In this case, Claripy will move on to the next backend in its list.

All backends must implement a *not* a claripy AST object (i.e., an integer or an object from a different backend). If

`convert()`

function. This function receives a claripy AST and should return an object that the backend can handle in its private methods. Backends should also implement a `_convert()`

method, which will receive anything that is `convert()`

or `_convert()`

receives something that the backend can't translate to a format that is usable internally, the backend should raise BackendError, and thus won't be used for that object. All backends must also implement any functions of the base `Backend`

abstract class that currently raise `NotImplementedError()`

.Claripy's contract with its backends is as follows: backends should be able to handle, in their private functions, any object that they return from their private *or* public functions. Claripy will never pass an object to any backend private function that did not originate as a return value from a private or public function of that backend. One exception to this is

`convert()`

and `_convert()`

, as Claripy can try to stuff anything it feels like into _convert() to see if the backend can handle that type of object.Backend Objects

To perform actual, useful computation on ASTs, Claripy uses backend objects. A

`BackendObject`

is a result of the operation represented by the AST. Claripy expects these objects to be returned from their respective backends, and will pass such objects into that backend's other functions.Last modified 5mo ago

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Outline

Claripy ASTs

Solvers

Claripy Backends

Backend Objects