Annual Computer Security Applications Conference (ACSAC) 2022

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One Fuzz Doesn’t Fit All: Optimizing Directed Fuzzing via Target-tailored Program State Restriction

Fuzzing is the de-facto default technique to discover software flaws, randomly testing programs to discover crashing test cases. Yet, a particular scenario may only care about specific code regions (for, e.g., bug reproduction, patch or regression testing)—spurring the adoption of directed fuzzing. Given a set of pre-determined target locations, directed fuzzers drive exploration toward them through distance minimization strategies that (1) isolate the closest-reaching test cases and (2) mutate them stochastically. However, these strate- gies are applied onto every explored test case—irrespective of whether they ever reach the targets—stalling progress on the paths where targets are unreachable. Accelerating directed fuzzing requires pri- oritizing target-reachable paths. To overcome the bottlneck of wasteful exploration in directed fuzzing, we introduce tripwiring: a lightweight technique to pre- empt and terminate the fuzzing of paths that will never reach target locations. By constraining exploration to only the set of target-reachable program paths, tripwiring curtails directed fuzzers’ search noise—while unshackling them from the high-overhead in- strumentation and bookkeeping of distance minimization—enabling directed fuzzers to obtain up to 99× higher test case throughput. 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++.

Prashast Srivastava
Purdue University

Stefan Nagy
University of Utah

Matthew Hicks
Virginia Tech

Antonio Bianchi
Purdue University

Mathias Payer

Paper (ACM DL)



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