Name:
SieveFuzz
Description:
Optimizing Directed Fuzzing via Target-tailored Program State Restriction
Professor — Lab:
Mathias PayerHexHive Group

Technical description:
We implement tripwiring-directed fuzzing as a prototype, Sieve-Fuzz, and evaluate it alongside the state-of-the-art directed fuzzers AFLGo, BEACON and the leading undirected fuzzer AFL++. Overall, across nine benchmarks, SieveFuzz’s tripwiring enables it to trigger bugs on an average 47% more consistently and 117% faster than AFLGo, BEACON and AFL++.
Papers:
Project status:
inactive — entered showcase: 2023-04-24 — entry updated: 2024-04-12

Source code:
Lab Github - last commit: 2023-04-10
Code quality:
This project has not yet been evaluated by the C4DT Factory team. We will be happy to evaluate it upon request.
Project type:
Framework