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Crab-llvm is a static analyzer that computes inductive invariants for LLVM-based languages based on the Crab library. It currently supports LLVM 3.8.


Crab-llvm is written in C++ and uses heavily the Boost library. The main requirements are:

  • C++ compiler supporting c++11
  • Boost
  • GMP

In linux, you can install requirements typing the commands:

 sudo apt-get install libboost-all-dev libboost-program-options-dev
 sudo apt-get install libgmp-dev
 sudo apt-get install libmpfr-dev	

Then, the basic compilation steps are:

 mkdir build && cd build
 cmake --build . --target crab && cmake ..
 cmake --build . --target llvm && cmake ..      
 cmake --build . --target install 

Crab-llvm provides several components that are installed via the extra target. These components can be used by other projects outside of Crab-llvm.

  • llvm-dsa: git clone https://github.com/seahorn/llvm-dsa.git

    llvm-dsa is the legacy DSA implementation from PoolAlloc. DSA (Data Structure Analysis) is a heap analysis described here and it is used by Crab-llvm to disambiguate the heap.

  • llvm-seahorn: git clone https://github.com/seahorn/llvm-seahorn.git

llvm-seahorn provides specialized versions of InstCombine and IndVarSimplify LLVM passes as well as a LLVM pass to convert undefined values into nondeterministic calls.

To include llvm-dsa and llvm-seahorn, type instead:

 mkdir build && cd build
 cmake --build . --target extra            
 cmake --build . --target crab && cmake ..
 cmake --build . --target llvm && cmake ..           
 cmake --build . --target install 

If you want to use the boxes domain then add -DUSE_LDD=ON.

If you want to use the apron domains then add -DUSE_APRON=ON.

To install crab-llvm with Boxes and Apron:

 mkdir build && cd build
 cmake --build . --target extra                 
 cmake --build . --target crab && cmake ..
 cmake --build . --target ldd && cmake ..
 cmake --build . --target apron && cmake ..
 cmake --build . --target llvm && cmake ..                
 cmake --build . --target install 

To run some regression tests:

 cmake --build . --target test-simple

To run tests you need to install lit and OutputCheck. In Linux:

$ apt-get install python-pip
$ pip install lit
$ pip install OutputCheck

Crab-llvm architecture

Crab-Llvm Architecture

Example 1

Consider the program test.c:

extern void __CRAB_assume (int);
extern void __CRAB_assert(int);
extern int  __CRAB_nd(void);

int main() {
  int k = __CRAB_nd();
  int n = __CRAB_nd();
  __CRAB_assume (k > 0);
  __CRAB_assume (n > 0);
  int x = k;
  int y = k;
  while (x < n) {
  __CRAB_assert (x >= y);
  __CRAB_assert (x <= y);  
  return 0;

Crab-llvm provides a Python script called crabllvm.py. Type the command:

crabllvm.py test.c

Important: the first thing that crabllvm.py does is to compile the C program into LLVM bitcode by using Clang. Since Crab-llvm is based on LLVM 3.8, the version of clang must be 3.8 as well.

If the above command succeeds, then the output should be something like this:

Invariants for main
  INVARIANTS: ({}, {})
  _2 =* ;
  _3 =* ;
  _4 = (-_2 <= -1);
  zext _4:1 to _call:32;
  _6 = (-_3 <= -1);
  zext _6:1 to _call1:32;
  x.0 = _2;
  y.0 = _2;
  goto _x.0;
  INVARIANTS: ({}, {_call -> [0, 1], _call1 -> [0, 1], _2-x.0<=0, y.0-x.0<=0, x.0-_2<=0, y.0-_2<=0, _2-y.0<=0, x.0-y.0<=0})
  INVARIANTS: ({}, {_call -> [0, 1], _call1 -> [0, 1], _2-x.0<=0, y.0-x.0<=0, _2-y.0<=0, x.0-y.0<=0})
  goto __@bb_1,__@bb_2;
  assume (-_3+x.0 <= -1);
  goto _10;
  INVARIANTS: ({}, {_call -> [0, 1], _call1 -> [0, 1], _2-x.0<=0, y.0-x.0<=0, _2-y.0<=0, x.0-y.0<=0, x.0-_3<=-1, _2-_3<=-1, y.0-_3<=-1})
  _11 = x.0+1;
  _br2 = y.0+1;
  x.0 = _11;
  y.0 = _br2;
  goto _x.0;
  INVARIANTS: ({}, {_call -> [0, 1], _call1 -> [0, 1], _br2-y.0<=0, _11-y.0<=0, _2-y.0<=-1, x.0-y.0<=0, x.0-_3<=0, _2-_3<=-1, y.0-_3<=0, _11-_3<=0, _br2-_3<=0, x.0-_11<=0, _2-_11<=-1, y.0-_11<=0, _br2-_11<=0, y.0-_br2<=0, _2-_br2<=-1, x.0-_br2<=0, _11-_br2<=0, _11-x.0<=0, _br2-x.0<=0, _2-x.0<=-1, y.0-x.0<=0})
  assume (_3-x.0 <= 0);
  y.0.lcssa = y.0;
  x.0.lcssa = x.0;
  goto _y.0.lcssa;
  INVARIANTS: ({}, {_call -> [0, 1], _call1 -> [0, 1], _2-x.0<=0, y.0-x.0<=0, _3-x.0<=0, y.0.lcssa-x.0<=0, x.0.lcssa-x.0<=0, _2-y.0<=0, x.0-y.0<=0, _3-y.0<=0, y.0.lcssa-y.0<=0, x.0.lcssa-y.0<=0, y.0-y.0.lcssa<=0, _2-y.0.lcssa<=0, x.0-y.0.lcssa<=0, _3-y.0.lcssa<=0, x.0.lcssa-y.0.lcssa<=0, x.0-x.0.lcssa<=0, _2-x.0.lcssa<=0, y.0-x.0.lcssa<=0, _3-x.0.lcssa<=0, y.0.lcssa-x.0.lcssa<=0})
  _14 = (y.0.lcssa-x.0.lcssa <= 0);
  zext _14:1 to _call3:32;
  assert (-_call3 <= -1);
  _16 = (-y.0.lcssa+x.0.lcssa <= 0);
  zext _16:1 to _call4:32;
  assert (-_call4 <= -1);
  @V_17 = 0;
  return @V_17;
  INVARIANTS: ({_14 -> true; _16 -> true}, {_call -> [0, 1], _call1 -> [0, 1], _call3 -> [1, 1], _call4 -> [1, 1], @V_17 -> [0, 0], _2-x.0<=0, y.0-x.0<=0, _3-x.0<=0, y.0.lcssa-x.0<=0, x.0.lcssa-x.0<=0, _2-y.0<=0, x.0-y.0<=0, _3-y.0<=0, y.0.lcssa-y.0<=0, x.0.lcssa-y.0<=0, y.0-y.0.lcssa<=0, _2-y.0.lcssa<=0, x.0-y.0.lcssa<=0, _3-y.0.lcssa<=0, x.0.lcssa-y.0.lcssa<=0, x.0-x.0.lcssa<=0, _2-x.0.lcssa<=0, y.0-x.0.lcssa<=0, _3-x.0.lcssa<=0, y.0.lcssa-x.0.lcssa<=0})

It shows the Control-Flow Graph analyzed by Crab together with the invariants inferred for function main that hold at the entry and and the exit of each basic block.

Note that Crab-llvm does not provide a translation from the basic block identifiers and variable names to the original C program. The reason is that Crab-llvm does not analyze C but instead the corresponding LLVM bitcode generated after compiling the C program with Clang. To help users understanding the invariants Crab-llvm provides an option to visualize the CFG of the function described in terms of the LLVM bitcode:

crabllvm.py test.c --llvm-view-cfg

and you should see a screen with a similar CFG to this one:

LLVM CFG of test.c

Since we are interested at the relationships between x and y after the loop, the LLVM basic block of interest is _y.0.lcssa and the variables are x.0.lcssa and y.0.lcssa, which are simply renamings of the loop variables x.0 and y.0, respectively.

With this information, we can look back at the invariants inferred by our tool and see the linear constraints:

x.0.lcssa-y.0.lcssa<=0, ... , y.0.lcssa-x.0.lcssa<=0

that implies the desired invariant x.0.lcssa = y.0.lcssa.

Crab Options

Crab-llvm analyzes programs with the zones domain as the default abstract domain. Users can choose the abstract domain by typing the option --crab-dom=VAL. The possible values of VAL are:

  • int: intervals
  • ric: reduced product of int and congruences
  • term-int: int with uninterpreted functions
  • zones: zones domain using sparse DBM in split normal form
  • oct: Elina's optimized octagon domains (only if -DUSE_APRON=ON)
  • pk: Apron's polka domain (only if -DUSE_APRON=ON)
  • boxes: disjunctive intervals based on LDDs (only if -DUSE_LDD=ON)
  • dis-int: disjunctive intervals based on Clousot's DisInt domain
  • term-dis-int: dis-int with uninterpreted functions
  • rtz: reduced product of term-dis-int with zones-split.

For domains without narrowing operator (for instance boxes, dis-int, and pk-apron), you need to set the option:


where N is the number of descending iterations (e.g., N=2).

You may want also to set the option:


where N is the number of fixpoint iterations before triggering widening (e.g., N=1).

The widening operators do not use thresholds by default. To use them, type the option


where N is the maximum number of thresholds.

We also provide the option --crab-track=VAL to indicate the level of abstraction. The possible values of VAL are:

  • int: reasons about integer and boolean scalars (LLVM registers).

  • ptr: reasons about int and pointer offsets.

  • arr: reasons about ptr and contents of pointers and arrays.

    If the level is arr then Crab-llvm uses the heap abstraction provided by llvm-dsa to partition the heap into disjoint regions. Each region is mapped to an array, and each LLVM load and store is translated to an array read and write operation, respectively. Then, it will use an array domain provided by Crab whose base domain is the one selected by option --crab-domain. If option --crab-singleton-aliases is enabled then Crab-llvm translates global singleton regions to scalar variables.

By default, all the analyses are run in an intra-procedural manner. Enable the option --crab-inter to run the inter-procedural version. Crab-llvm implements a standard two-phase algorithm in which the call graph is first traversed from the leaves to the root while computing summaries and then from the root the leaves reusing summaries. Each function is executed only once. The analysis is sound with recursive functions but imprecise. The option --crab-print-summaries displays the summaries for each function. The inter-procedural analysis is specially important if reasoning about memory contents is desired.

Crab-llvm provides the option --crab-backward to enable an iterative forward-backward analysis that might produce more precise results. The backward analysis computes necessary preconditions of the error states (if program is annotated with assertions) which are used to refine the set of initial states so that the forward analysis can refine its results.

Note that apart from inferring invariants or preconditions, Crab-llvm allows checking for assertions. To do that, programs must be annotated with __CRAB_assert(c) where c is any expression that evaluates to a boolean. Note that __CRAB_assert must be defined as an extern function so that Clang does not complain:

extern void __CRAB_assert(int);

Then, you can type:

crabllvm.py test.c --crab-check=assert

and you should see something like this:

user-defined assertion checker using SplitDBM
2  Number of total safe checks
0  Number of total error checks
0  Number of total warning checks

Finally, to make easier the communication with other LLVM-based tools, Crab-llvm can output the invariants by inserting them into the LLVM bitcode via verifier.assume instructions. The option --crab-add-invariants=block-entry injects the invariants that hold at each basic block entry while option --crab-add-invariants=after-load injects the invariants that hold right after each LLVM load instruction. The option all injects invariants in all above locations. To see the final LLVM bitcode just add the option -o out.bc.

Example 2

Consider the next program:

    extern int __CRAB_nd(void);
    int a[10];
    int main (){
       int i;
       for (i=0;i<10;i++) {
         if (__CRAB_nd ())
       int res = a[i-1];
       return res;

and type

crabllvm.py test.c --crab-track=arr --crab-add-invariants=all -o test.crab.bc
llvm-dis test.crab.bc

The content of test.crab.bc should be similar to:

    define i32 @main() #0 {
       br label %loop.header
    loop.header:   ; preds = %loop.body, %entry
       %i.0 = phi i32 [ 0, %entry ], [ %_br2, %loop.body ]
       %crab_2 = icmp ult i32 %i.0, 11
       call void @verifier.assume(i1 %crab_2) #2
       %_br1 = icmp slt i32 %i.0, 10
       br i1 %_br1, label %loop.body, label %loop.exit
    loop.body:   ; preds = %loop.header
       call void @verifier.assume(i1 %_br1) #2
       %crab_14 = icmp ult i32 %i.0, 10
       call void @verifier.assume(i1 %crab_14) #2
       %_5 = call i32 (...)* @__CRAB_nd() #2
       %_6 = icmp eq i32 %_5, 0
       %_7 = sext i32 %i.0 to i64
       %_. = getelementptr inbounds [10 x i32]* @a, i64 0, i64 %_7
       %. = select i1 %_6, i32 5, i32 0
       store i32 %., i32* %_., align 4
       %_br2 = add nsw i32 %i.0, 1
       br label %loop.header
    loop.exit:   ; preds = %loop.header
       %_11 = add nsw i32 %i.0, -1
       %_12 = sext i32 %_11 to i64
       %_13 = getelementptr inbounds [10 x i32]* @a, i64 0, i64 %_12
       %_ret = load i32* %_13, align 4
       %crab_23 = icmp ult i32 %_ret, 6
       call void @verifier.assume(i1 %crab_23) #2
       ret i32 %_ret

The special thing about the above LLVM bitcode is the existence of @verifier.assume instructions. For instance, the instruction @verifier.assume(i1 %crab_2) indicates that %i.0 is between 0 and 10 at the loop header. Also, @verifier.assume(i1 %crab_23) indicates that the result of the load instruction at block loop.exit is between 0 and 5.

Known limitations of the translation from bitcode to Crab CFG

  • Ignore floating point operations.

Analysis limitations

Well, there are many. Most of these limitations are coming from Crab. Here some of them:

  • Crab numerical domains mostly reason about linear arithmetic.

  • Most Crab numerical domains reason about infinite integers.

  • There are several Crab numerical domains that compute disjunctive invariants but they are still limited in terms of expressiveness to keep them tractable.

  • The interprocedural analysis is summary-based but it's context-insensitive.

  • Crab does not provide any pointer or shape analysis but it provides a simple nullity analysis that can tell whether pointer may be null or not.

  • Crab-llvm can reason about pointer's contents only if llvm-dsa can infer statically that a pointer points to a memory region that behaves as a C arrays (i.e., consecutive sequence of bytes where elements must have compatible types and offset must be multiple of the element type size). Once a logical array has been identified, Crab-llvm uses one of the Crab array domains to reason about their contents. Currently, it only supports array smashing.