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Coverage-guided fuzzing detects memory corruption in Python C extensions by using libFuzzer to explore code paths and integrating AddressSanitizer to identify memory leaks and buffer overflows. You define an input harness to stress-test target functions.

Coverage-guided fuzzing is used in Python testing to intelligently explore code paths and maximize test coverage, identifying elusive runtime crashes and memory corruption bugs that traditional unit testing often misses.

Yes, you need a configured environment with clang and appropriate sanitizer flags to fuzz Python code accurately. AddressSanitizer integration is required to detect memory leaks and buffer overflows in native C extensions.

The best way to detect buffer overflows when fuzzing C extensions is integrating AddressSanitizer with libFuzzer. This combination performs deep code path exploration to accurately expose memory safety issues and runtime crashes.

To set up an input harness for fuzzing a Python script, define a target function that processes generated inputs, then run the fuzzer to stress-test it. This exposes memory corruption and crashes from malformed input.

Traditional unit testing misses memory corruption bugs in Python because it lacks coverage-guided code path exploration. Fuzzing with libFuzzer intelligently generates inputs to trigger elusive memory leaks and buffer overflows in C extensions.