aengelke / mishegos

A differential fuzzer for x86 decoders

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

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)

Run an analysis/filter pass group on the results:

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

Performance notes

All numbers below correspond to the following run:

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

Within Docker:

• On a Linux server (40 cores, 128GB RAM):
  • 3.5M outputs/minute
  • 5 cores pinned
• On a 2018 Macbook Pro (2+2 cores, 16GB RAM):
  • 300K outputs/minute
  • (All) 4 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
  • Use a faster serialization format than JSON
• 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