CS计算机代考程序代写 c++ fuzzing Lab 3: Random Input Generation

Lab 3: Random Input Generation

CIS 547: Software Analysis

Lab 3: Random Input Generation (Summer 2021)

Synopsis

Building a coverage-guided random input generator a.k.a. “fuzzer” for testing C programs.

Objective

In this lab, you will develop a fuzzer for testing C programs. Fuzzing is a popular software
testing technique wherein the program under test is fed randomly generated inputs. Such inputs
help uncover a wide range of security-critical and crashing bugs in programs. For this purpose,
your fuzzer will begin with seed inputs, and generate new inputs by mutating previous inputs. It
will use output from previous rounds of test as feedback to direct future test generation. You will
use the code coverage metrics implemented in Lab 2 to help select interesting inputs for your
fuzzer to mutate.

Pre-Requisites

● Watch the video lectures corresponding to the module on “Random Testing”. The lectures
introduce various terminology used throughout this lab such as seed inputs, mutations,
and feedback-directed testing.

Setup

Step 1. The skeleton code for Lab 3 is located under /cis547vm/lab3/. We will frequently refer
to the top level directory for Lab 3 as lab3 when describing file locations for the lab.

This lab builds off the work you did in Lab2. Start out by copying your solution from
lab2/src/Instrument.cpp to lab3/src/Instrument.cpp.

Then, run the following commands setup the lab:

/cis547vm$ cd lab3
/cis547vm/lab3$ mkdir build && cd build
/cis547vm/lab3/build$ cmake ..
/cis547vm/lab3/build$ make

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CIS 547: Software Analysis

Running make will build the fuzzer tool, which we will use to feed randomized input (that you
will generate) into compiled C programs that will run with your sanitizer from Lab 2. In
particular, you should notice the InstrumentPass.so and fuzzer files in your lab3/build
directory after running make.

Step 2. Next, create lab3/test/fuzz_output if it does not exist. Then, prepare the programs
under lab3/test for fuzzing. This will be done by first instrumenting the .c files as you did in
Lab 2. Here, we are instrumenting sanity.c:

/cis547vm$ cd lab3/test

/cis547vm/lab3/test$ mkdir fuzz_output

/cis547vm/lab3/test$ clang -emit-llvm -S -fno-discard-value-names -c -o
sanity.ll sanity.c -g

/cis547vm/lab3/test$ opt -load ../build/InstrumentPass.so -Instrument -S
sanity.ll > sanity.instrumented.ll

/cis547vm/lab3/test$ clang -o sanity -L${PWD}/../build -lruntime -lm
sanity.instrumented.ll

Alternatively, you can run make in the lab3/test directory to instrument all .c files in the test
directory. You can now run your fuzzer on the sanity executable:

/cis547vm/lab3/test$ timeout 1 ../build/fuzzer ./sanity fuzz_input fuzz_output

Notice the ./ before sanity as the fuzzer will not run without it. Since we can theoretically
generate random input forever, we wrap the fuzzer in a timeout that will stop program execution
after a specified interval. In the above case, we run for 1 second.

Expect fuzz_output to get populated with several files that contain the random inputs that cause
crashes, e.g,

fuzz_output/failure/input1
fuzz_output/failure/input2

fuzz_output/failure/inputN

with N being the last case that caused a crash before the timeout. We will also output test cases

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CIS 547: Software Analysis

that resulted in a successful program run under fuzz_output/success/; however, since it’s
likely you will get many successful program runs before a failure, we write every 1000th
successful run.

You will also see the seed of the latest run saved in fuzz_output/randomSeed.txt. If you wish
to provide your own seed, e.g. to reproduce a previous fuzzer run, you may do so by passing it as
an additional argument to the above command:

/cis547vm/lab3/test$ timeout 1 ../build/fuzzer ./sanity fuzz_input fuzz_output
112358

For sanity, the fuzzer will find several crashes. However, to get your fuzzer to work on the
other programs, you will need to complete lab3/src/Mutate.cpp. The fuzzer you implement
will randomly generate new inputs starting from the initial seed file that is located in
fuzz_input/seed.txt. You will also use information about the program run, specifically,
coverage information, to drive the fuzzing.

Lab Instructions

A full-fledged fuzzer consists of three key features: i) test case generation matching the grammar
of the program input, ii) strategies to mutate test inputs to increase code coverage, iii) a feedback
mechanism to help drive the types of mutations used.

Mutation-Fuzzing Primer. Consider the following code that reads some string input from the
command line:

int main() {
char input[65536];
fgets(input, sizeof(input), stdin);
int x = 13;
int z = 21;

if (strlen(input) % 13 == 0) {
z = x / 0;

}

if (strlen(input) > 100 && input[25] == ‘a’) {
z = x / 0;

}

return 0;
}

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CIS 547: Software Analysis

We have two very obvious cases that cause divide-by-zero errors in the program: (1) if the length
of the program input is divisible by 13, and (2) if the length of the input is greater than 100 and
the 25th character in the string is an ‘a’. Now, let’s imagine that this program is a black box, and
we can only search for errors by running the code with different inputs.

We would likely try a random string, say “abcdef”, which would give us a successful run. From
there, we could take our first string as a starting point and add some new characters, “ghi”,
giving us “abcdefghi”. Here we have mutated our original input string to generate a new test
case. We might repeat this process, finally stumbling on “abcdefghijklm” which is divisible by
13 and causes the program to crash.

How about the second case? We could keep inserting characters onto the end of our string, which
would eventually get us some large string that satisfies the first condition of the if statement
(input length greater than 100), but we need to perform an additional type of
mutation—randomly changing characters in the string—to eventually satisfy the second
condition in the if statement.

Through various mutations on an input string, we ended up exhausting all program execution
paths, i.e., more varied mutation in the input increased our code coverage. In its simplest form,
this is exactly what a fuzzer does. You may take a look at the Mutation-Based Fuzzing chapter in
the Fuzzing Book.

Feedback-Directed Fuzzing. We’ve seen how randomized testing can find bugs and is a useful
software analysis tool. The previous section describes a brute force generation of test cases; we
simply perform random mutations hoping that we find a bug. This results in a lot of test cases
being redundant, and therefore unnecessary.

We can gather additional information about a program’s execution and use it as feedback to our
fuzzer. The following figure shows at a high level what this process looks like:

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https://www.fuzzingbook.org/html/MutationFuzzer.html

CIS 547: Software Analysis

Generating new, interesting seeds is the goal of feedback directed fuzzing. What does interesting
mean? We might consider whether a test increases code coverage. If so, we have found new
execution pathways that we want to continue to explore. Another test might significantly
increase program runtime, in which case we might discover some latent performance bugs. In
both cases, the tests increased our knowledge of the program; hence, we insert these tests into
our set of seeds and use them as a starting point for future test generation.

Building the Fuzzer. In this lab, you will implement a mutation function mutate in
lab3/src/Mutate.cpp, which will perform some form of mutation on the input. You will decide
which mutation strategy or strategies to use to perform the mutation. Feel free to explore the
source code of the programs under lab3/test like easy.c or path.c to get a better idea of how
to implement the mutation function.

The fuzzer will start by mutating the seed values based on the mutation you’ve selected on the
command line. The mutated value will be run on the input program, and feedback will be
provided based on the coverage of that seed. You will then decide if this is an interesting seed
and insert it as an additional seed if so in the SeedInput vector. This causes the mutated input to
again be mutated. This process continues until the fuzzer gets interrupted (via timeout, or on the
terminal by Ctrl+C). The following code snippet illustrates this main loop:

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CIS 547: Software Analysis

initialize(OutDir);

if (readSeedInputs(SeedInputDir)) {
fprintf(stderr, “Cannot read seed input directory\n”);
return 1;

}

while (true) {
std::string SC = selectInput();
auto Mutant = mutate(SC);
test(Target, Mutant, OutDir);
feedBack(Target, Mutant);

}

Fuzzing the Binaries. We have provided several binary programs hidden1, hidden2, etc. These
programs serve as more challenging test cases for your fuzzer. We will not provide the raw
source; instead, your mutations should be versatile enough to find test cases that crash these
programs.

Possible Mutations. The following is a list of potential suggestions for your mutations:

● Replace bytes with random values
● Swap adjacent bytes
● Cycle through all values for each byte
● Remove a random byte
● Insert a random byte

Feel free to play around with additional mutations, and see if you can speed up the search for
bugs on the binaries. You may use the C++ function rand() to generate a random integer.

You will notice that different programs will require different strategies, or that in some cases you
may even have to switch between different mutation strategies in the middle of the fuzzing
process. You are expected to include a mechanism that will choose the best strategy for the input
program based on the coverage feedback.

In short, the lab consists of the following tasks in Mutate.cpp:
1. Complete the selectInput function, which selects a mutant string from the SeedInputs

vector.

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https://en.cppreference.com/w/cpp/numeric/random/rand

CIS 547: Software Analysis

2. Complete the mutate function so that it returns a Mutant of the original string. Take
inspiration from the aforementioned list of mutations.

3. Decide whether the mutation was interesting based on success or failure of the program
and the code coverage. Again, you may follow our groundwork and fill in feedBack.

4. Insert the Mutant into the pool of SeedInput to drive further mutation.

Code Coverage Metric. Recall that you have a way of checking how much of a particular
program gets executed with the coverage information output by your sanitizer. A .cov file will
get generated from your sanitizer in the working directory for the program that is getting fuzzed.
For example, running:

$ ../build/fuzzer ./sanity fuzz_input fuzz_output

will create and update sanity.cov in the working directory that your feedBack function should
read and use between each test.

A Few More Hints. Start small. Implement one mutation strategy in the mutate function to try
and crash some of the easier test cases. Once successful, you can move on to implementing
multiple strategies and choosing between them based on the feedback you get.

Do not be afraid to keep state between rounds of the fuzzing. You may want to try each of your
mutation strategies initially to see which one generates a test that increases code coverage, and
then exploit that strategy. We expect this fuzzer to be versatile; it should generate crashing tests
on all programs in lab3/test.

Items to Submit

Submit file Mutate.cpp. Optionally, you may submit Instrument.cpp, and/or randomSeed.txt
if you would like us to use a specific seed in grading the provided binary programs.

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