FrameShift: Learning to Resize Fuzzer Inputs Without Breaking Them
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| Format: | Preprint |
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2025
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| _version_ | 1866913932027887616 |
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| author | Green, Harrison Goues, Claire Le Brown, Fraser |
| author_facet | Green, Harrison Goues, Claire Le Brown, Fraser |
| contents | Coverage-guided fuzzers are powerful automated bug-finding tools. They mutate program inputs, observe coverage, and save any input that hits an unexplored path for future mutation. Unfortunately, without knowledge of input formats--for example, the relationship between formats' data fields and sizes--fuzzers are prone to generate destructive frameshift mutations. These time-wasting mutations yield malformed inputs that are rejected by the target program. To avoid such breaking mutations, this paper proposes a novel, lightweight technique that preserves the structure of inputs during mutation by detecting and using relation fields.
Our technique, FrameShift, is simple, fast, and does not require additional instrumentation beyond standard coverage feedback. We implement our technique in two state-of-the-art fuzzers, AFL++ and LibAFL, and perform a 12+ CPU-year fuzzer evaluation, finding that FrameShift improves the performance of the fuzzer in each configuration, sometimes increasing coverage by more than 50%. Furthermore, through a series of case studies, we show that our technique is versatile enough to find important structural relationships in a variety of formats, even generalizing beyond C/C++ targets to both Rust and Python. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_05421 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FrameShift: Learning to Resize Fuzzer Inputs Without Breaking Them Green, Harrison Goues, Claire Le Brown, Fraser Cryptography and Security Coverage-guided fuzzers are powerful automated bug-finding tools. They mutate program inputs, observe coverage, and save any input that hits an unexplored path for future mutation. Unfortunately, without knowledge of input formats--for example, the relationship between formats' data fields and sizes--fuzzers are prone to generate destructive frameshift mutations. These time-wasting mutations yield malformed inputs that are rejected by the target program. To avoid such breaking mutations, this paper proposes a novel, lightweight technique that preserves the structure of inputs during mutation by detecting and using relation fields. Our technique, FrameShift, is simple, fast, and does not require additional instrumentation beyond standard coverage feedback. We implement our technique in two state-of-the-art fuzzers, AFL++ and LibAFL, and perform a 12+ CPU-year fuzzer evaluation, finding that FrameShift improves the performance of the fuzzer in each configuration, sometimes increasing coverage by more than 50%. Furthermore, through a series of case studies, we show that our technique is versatile enough to find important structural relationships in a variety of formats, even generalizing beyond C/C++ targets to both Rust and Python. |
| title | FrameShift: Learning to Resize Fuzzer Inputs Without Breaking Them |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2507.05421 |