mishegos

A differential fuzzer for x86 decoders.

Usage

Start with a clone, including submodules:

git clone --recurse-submodules https://github.com/trailofbits/mishegos

Building

mishegos is most easily built within Docker:

docker build -t mishegos .

Alternatively, you can try building it directly.

Make sure you have binutils-dev (or however your system provides libopcodes) installed:

make
# or
make debug

Running

Run the fuzzer for a bit:

./src/mishegos/mishegos ./workers.spec > /tmp/mishegos

mishegos checks for three environment variables:

• V=1 enables verbose output on stderr
• D=1 enables the "dummy" mutation mode for debugging purposes
• M=1 enables the "manual" mutation mode (i.e., read from stdin)
• MODE=mode can be used to configure the mutation mode in the absence of D and M
  • Valid mutation modes are sliding (default), havoc, and structured

Convert mishegos's raw output into JSONL suitable for analysis:

./src/mish2jsonl/mish2jsonl /tmp/mishegos > /tmp/mishegos.jsonl

mish2jsonl checks for V=1 to enable verbose output on stderr.

Run an analysis/filter pass group on the results:

./src/analysis/analysis -p same-size-different-decodings < /tmp/mishegos.jsonl > /tmp/mishegos.interesting

Generate an ugly pretty visualization of the filtered results:

./src/mishmat/mishmat < /tmp/mishegos.interesting > /tmp/mishegos.html
open /tmp/mishegos.html

Contributing

We welcome contributors to mishegos!

A guide for adding new disassembler workers can be found here.

Performance notes

All numbers below correspond to the following run:

V=1 timeout 60s ./src/mishegos/mishegos ./workers.spec > /tmp/mishegos

Outside Docker:

• On a Linux desktop (Ubuntu 20.04, Ryzen 5 3600, 32GB DDR4):
  • Commit d80063a
  • 8 workers (no udis86) + 1 mishegos fuzzer process
  • 8.7M outputs/minute
  • 9 cores pinned

TODO

• Performance improvements
  • Break cohort collection out into a separate process (requires re-addition of semaphores)
  • Maybe use a better data structure for input/output/cohort slots
• Add a scaling factor for workers, e.g. spawn N of each worker
• Pre-analysis normalization (whitespace, immediate representation, prefixes)
• Analysis strategies:
  • Filter by length, decode status discrepancies
  • Easy: lexical comparison
  • Easy: reassembly + effects modeling (maybe with microx?)
• Scoring ideas:
  • Low value: Flag/prefix discrepancies
  • Medium value: Decode success/failure/crash discrepancies
  • High value: Decode discrepancies with differing control flow, operands, maybe some immediates
• Visualization ideas:
  • Basic but not really basic: some kind of mouse-over differential visualization