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Individual AI skill · AI for Inteligencia e Investigación

atheris

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.

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Free ZIP · One canonical method · Usage example · Installation guide · Licence notices

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When to use it

| 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:

  • Fuzzing pure Python code with coverage guidance
  • Testing Python C extensions for memory corruption
  • Integration with libFuzzer ecosystem is desired
  • AddressSanitizer support is needed

Mistakes to avoid

| Problem | Cause | Solution | |---------|-------|----------| | No coverage increase | Poor seed corpus or target not instrumented | Add better seeds, verify instrument_imports() | | Slow execution | ASan overhead or large inputs | Reduce max_len, use ASAN_OPTIONS=fast_unwind_on_malloc=1 | | Import errors | Modules imported before instrumentation | Move imports inside instrument_imports() context | | Segfault without ASan output | Missing LD_PRELOAD | Set LD_PRELOAD to asan_with_fuzzer.so path | | Build failures | Wrong compiler or missing flags | Verify CC, CFLAGS, and clang version |

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AI for Inteligencia e Investigación

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skills/transversal/knowledge/trailofbits-skills/plugins/testing-handbook-skills/skills/atheris
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Extracted from SKILL.md
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