What problem does it solve? Learning to find memory corruption vulnerabilities through fuzzing requires structured guidance across many tools, harness strategies, and triage workflows that are otherwise scattered across books, docs, and blog posts. ## Core Features & Use Cases - Structured fuzzing curriculum: A multi-day course covering target selection, corpus generation, coverage-guided fuzzing with AFL++, in-process fuzzing with Google FuzzTest, and Honggfuzz. - Hands-on labs with real targets: Exercises fuzz real software such as GStreamer, libWebP, dlib, and OpenSSL, with sanitizers (ASan/UBSan) enabled to surface memory corruption. - Crash triage and deduplication: Covers afl-tmin minimization, casr-afl clustering, and exploitability analysis of fuzzer output. - Use Case: A security student sets up an AFL++ campaign against an image parser, minimizes the seed corpus with afl-cmin, runs parallel master/slave instances, and triages the resulting ASan crashes to identify an exploitable heap overflow. ## Quick Start Walk me through setting up an AFL++ fuzzing campaign with AddressSanitizer against a C image parser, including seed corpus creation and crash triage.
