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A differential fuzzer for x86 decoders
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woodruffw mishegos: Remove MISHEGOS_NWORKERS (#9)
mishegos: Remove MISHEGOS_NWORKERS

Removes `MISHEGOS_NWORKERS` in favor of a new `nworkers` field
in `mishegos_config`. Initialization has been reordered to
accommodate storing/loading that field.
Latest commit 09defba Nov 3, 2019


Build Status

A differential fuzzer for x86 decoders.



Start with a clone, including submodules:

git clone --recurse-submodules


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:

# or
make debug


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


  • 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
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