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Improving AFL by using Intel PT to collect branch information
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README.md
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README.md

PTfuzzer

Introduction

  • Binary-only fuzzing. We propose a new greybox fuzzer to fuzz any binaryonly softwares and do not need any source code. In situations where source code is unavailable, compile-time instrumentation and thorough program analysis is impossible, and fuzzers like AFL, AFLFast and VUzzer will be of no use. Our approach can gracefully handle these situations and fuzz binaries as usual.
  • Fast feedback mechanism. We introduce a much faster feedback mechanism. As mentioned above, though previous works tried hard to solve the problem of source code reliance, they all suffer from considerable performance overhead, especially QAFL and TriforceAFL. We utilize fast hardware feedback directly from CPU, and deal with binary-only fuzzing in a faster way than QAFL. The performance overhead of our fuzzer is smaller than QAFL according to our experiments.
  • Accurate coverage feedback. We propose a more accurate measurement for code coverage feedback. Compile-time instrumentation and random id assignment of basic blocks will measure code coverage inaccurately. We use actual run-time addresses of basic blocks to trace transitions between basic blocks and can provide real control flow information of running code.
  • PTfuzzer. We implement a prototype called PTfuzzer based on these insights. And our experiments show that PTfuzzer can deal with binary-only fuzzing quickly and accurately.

Requirements

  • Linux kernel >= 4.13.0. (Ubuntu 16.04.4 is OK in our test.)
  • Intel CPU i5/6/7-x000, x >= 5
  • libcapstone
  • python-cle

You may use check-dep.sh to install the dependent packets.

How to install

cd ptfuzzer/
mkdir build
cd build
cmake ../ -DPREFIX=.
make
make install 

This will install all python scripts and binary files to bin in the current directory.

How to run

  • You need to open the performance switch of the system everytime you reboot the system or simply run config-run.sh.
su
echo core >/proc/sys/kernel/core_pattern
cd /sys/devices/system/cpu
echo performance | tee cpu*/cpufreq/scaling_governor
  • Prepare a your own target program and initial seed files
cd ptfuzzer/build
python ./bin/ptfuzzer.py "-i your/input/directory -o your/output/directory" "your/target/program -arguement"
  • e.g. python ./bin/ptfuzzer.py "-i ./test/in -o ./test/out" "./test/readelf -a"
  • Please refer to ptfuzzer/afl-pt/doc/ if you need more information and about AFL arguements

Configurate runtime parameters

You can edit a config file to control the runtime parameters of ptfuzzer. The config file must be named ptfuzzer.conf, and it can be put in the current working directory or /etc/. Here is an example:

#BRANCH_MODE=TNT_MODE
BRANCH_MODE=TIP_MODE
MEM_LIMIT=100            # afl -m argument
PERF_AUX_BUFFER_SIZE=32  # the size of buffer used to store PT packets.

BRANCH_MODE controls the methods ptfuzzer uses to collect branch information. In TIP_MODE, only the control flow change encoded in the TIP packets are recorded, while TNT_MODE also includes the conditional branch encoded in the TNT packets.

MEM_LIMIT controls the memory limits of the target program. It is the "-m" arguments passed to afl.

PERF_AUX_BUFFER_SIZE controls the size of buffer ptfuzzer allocates for storing PT packest. The PT packets may be truncated if the buffer size is not big enough.

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