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Atheris is a coverage-guided Python fuzzer based on libFuzzer.
Passed 21 of 21 test cases with the skill, and 16 of 21 without it.
and 6 more in the full report
5 fixed17 passed either way0 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 4 test cases the skill fixed. Same model, same prompt, with and without the skill.
AddressSanitizer prints stack traces with raw hex addresses instead of source file names and line numbers when fuzzing in Docker. What environment variable should be set to point to the LLVM symbolizer tool?
| Checked | Without skill | With skill |
|---|---|---|
| Sets ASAN_SYMBOLIZER_PATH environment variable to llvm-symbolizer binary | Missed | Passed |
The instructions your model reads when the skill loads.
Atheris is a coverage-guided Python fuzzer built on libFuzzer. It enables fuzzing of both pure Python code and Python C extensions with integrated AddressSanitizer support for detecting memory corruption issues.
| Fuzzer | Best For | Complexity |
|---|---|---|
| Atheris | Python code and C extensions | Low-Medium |
| Hypothesis | Property-based testing | Low |
| python-afl | AFL-style fuzzing | Medium |
Choose Atheris when:
import sys
import atheris
@atheris.instrument_func
def test_one_input(data: bytes):
if len(data) == 4:
if data[0] == 0x46: # "F"
if data[1] == 0x55: # "U"
if data[2] == 0x5A: # "Z"
if data[3] == 0x5A: # "Z"
raise RuntimeError("You caught me")
def main():
atheris.Setup(sys.argv, test_one_input)
atheris.Fuzz()
if __name__ == "__main__":
main()
test cases passed, vs 16 / 21 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.