Objective

In this lab, you will implement a constraint-based analysis to detect exploitable divide-by-zero bugs. A bug is exploitable if hackers can control inputs or environments, thereby triggering unintended behaviors (e.g., denial-of-service) through the bug. For example, a recently reported divide-by-zero bug in the Linux kernel can be exploitable and crash the system; another example was the Log4Shell incident. You will design a static analysis that detects such bugs by combining reaching definition analysis and taint analysis using a datogon engine, souffle.

Setup

The skeleton code for Lab8 is located under cis547/lab8/. We will frequently refer to the top level directory for Lab 8 as lab8 when describing file locations for the lab. Open the lab8 directory in VSCode following the Instructions from Course VM document

The following commands set up the lab, using the Cmake/Makefile pattern seen before.

/lab8$ mkdir build && cd build
/lab8/build$ cmake ..
/lab8/build$ make

The above command will generate an executable file ‘constraint’ in build directory that extracts facts about the program that will be used by src/analysis.dl to check if the input program has an exploitable divide-by-zero bug:

/lab8$ cd ./test
/lab8/test$ clang -emit-llvm -S -fno-discard-value-names -c simple0.c
/lab8/test$ mkdir -p simple0/input simple0/output
/lab8/test$ ../build/constraint simple0.ll ./simple0/input
/lab8/test$ souffle -F ./simple0/input -D ./simple0/output ../src/analysis.d

After completing the lab implementation, you should see the instruction that could potentially cause a divide-by-zero in ./simple0/output/alarm.csv.

Lab Instructions

In this lab, you will design a reaching definition analysis and taint analysis using datalog.

The main tasks are to design the analysis in the form of logical rules as a Datalog program, and implement a function that extracts logical relations form a test program in the form of Datalog facts for each LLVM instruction.

We will then use the datalog rules with the facts you extracted from each instruction in a program to find any exploitable divide-by-zero errors.

In short, the lab consists of the following tasks:

  1. Write Datalog rules in analysis.dl for taint analysis.
  2. Implement the extractContraints function in Extractor.cpp that extracts Datalog facts from LLVM IR Instruction and dumps them to appropriate .facts files. Detailed instructions provided in comments.

Relations for Datalog Analysis

The skeleton code in Extractor.cpp provides the definitions of various functions that will help you dump the necessary Datalog relations declared in src/analysis.dl file.

Here we will explain what each of the datalog relation mean.

The relations for def and use of variables are as follows:

  • def(var, inst): Variable var is defined at instruction inst.
  • use(var, inst): Variable var is used in instruction inst.

The relations for the reaching definition analysis are as follows:

  • kill(curr_inst, old_inst): Instruction curr_inst kills definition at instruction old_inst.
  • next(curr_inst, next_inst): Instruction next_inst is an immediate successor of instruction curr_inst.
  • in(inst, def_inst): Definition at defining instruction def_inst may reach the program point just before instruction inst.
  • out(inst, def_inst): Definition at defining instruction def_inst may reach the program point just after instruction inst.

Note that the kill relation can be derived by using the def relation by writing a Datalog rule.

The relations for the taint analysis are as follows:

  • taint(inst) : There exists a function call at intruction inst that reads a tainted input.
  • edge(from, to): There exists an immediate data-flow from instruction from to instruction to.
  • Path(from, to): There exists a transitive tainted data-flow from instruction from to instruction to.
  • sanitizer(inst) : There exists a function call at intruction inst that sanitizes a tainted input.
  • div(denom, inst) : There exists a division operation at instruction inst whose divisor is variable denom.
  • alarm(inst) : There exists a potential exploitable divide-by-zero error at instruction inst.

Assume that input programs may contain function calls to tainted_input and sanitizer that read and sanitize a tainted input, respectively. The final output relation for potential bug reports is alarm.

You will use these relations to build rules for the definition analysis and taint analysis in analysis.dl.

Encoding LLVM Instruction in Datalog

Recall that, in LLVM IR, values and instructions are interchangable. Therefore, all variables X, Y, and Z are an instance of LLVM’s Value class.

Assume that input C programs do not have pointer variables. Therefore, we abuse pointer variables in LLVM IR as their dereference expressions. Consider the following simplified LLVM program from a simple C program int x = 0; int y = x;:

x = alloca i32          ; I0
y = alloca i32          ; I1
store i32 0, i32* x     ; I2
a = load i32, i32* x    ; I3
store i32 a, i32* y     ; I4

We ignore alloca instructions and consider that each store instruction defines the second argument.

In the case of the above example, you should have addDef(I0,I2), because I0 corresponds to x in LLVM IR and I2 defines the variable x. Likewise, consider each load instruction uses the argument. In the example, you should have addUse(I0,I3) and addDef(I3,I3) because load instructions define a non-pointer variable which is represented as the instruction itself in LLVM. Finally, you should have addUse(I3,I4) and addDef(I1,I4) for instruction I4.

Defining Datalog Rules

You will write your Datalog rules for taint analysis in src/analysis.dl using the relations above.

Please refer to the souffle’s documentation for the datalog language and its syntax.

Here’s a quick example to help you get started.

Consider an example Datalog rule:

A(X, Y) :- B(X, Z), C(Z, Y).

It adds a rule which says that there is a relation A between X and Y if, there is a relation B between X and some Z and there is also a relation C between that Z and Y.

Extracting Datalog Facts

You will need to implement the function extractConstraints in Extractor.cpp to extract Datalog facts for each LLVM instruction. The skeleton code provides a couple of auxiliary functions in lab8/src/Extract.cpp and lab8/src/Utils.cpp help you with this task:

  • void addX(const InstMapTy &InstMap, ...)
    • X denotes the name of a relation. These functions add a fact to the facts file for X.
    • It takes InstMap that encodes each LLVM instruction as an integer. This map is initialized in the main function.
  • vector<Instruction*> getPredecessors(Instruction *I)
    • Returns a set of predecessors of a given LLVM instruction I.
  • bool isTaintedInput(CallInst *CI)
    • Checks whether a given LLVM call instruction CI reads a tainted input or not.
  • bool isSanitizer(CallInst *CI)
    • Checks whether a given LLVM call instruction CI sanitizes a tainted input or not.

Miscellaneous

For debugging, after you run constraint you can inspect the ./test/simple0/input for files containing the extracted relations.

Format of Input Programs

Input programs in this lab are assumed to have only sub-features of the C language as follows:

  • All values are integers (i.e., no floating points, pointers, structures, enums, arrays, etc). You can ignore other types of values.
  • Assume that there is no function call to a function with a void return type.
  • You must handle the function calls to tainted_input and sanitizer in a special way which represents their actions as described previously.

Example Input and Output

Your analyzer should run on LLVM IR. For example:

/lab8$ cd ./test
/lab8/test$ make loop0

If the input program has exploitable divide-by-zero errors, then you should see entries in loop0/output/alarm.csv.

%div

Submission

Once you are done with the lab, you can create a submission.zip file by using the following command:

lab8$ make submit
...
submission.zip created successfully.

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