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| Format: | Preprint |
| Published: |
2025
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| Online Access: | https://arxiv.org/abs/2511.03995 |
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| _version_ | 1866917064466235392 |
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| author | Lin, Shiyin |
| author_facet | Lin, Shiyin |
| contents | Software fuzzing has become a cornerstone in automated vulnerability discovery, yet existing mutation strategies often lack semantic awareness, leading to redundant test cases and slow exploration of deep program states. In this work, I present a hybrid fuzzing framework that integrates static and dynamic analysis with Large Language Model (LLM)-guided input mutation and semantic feedback. Static analysis extracts control-flow and data-flow information, which is transformed into structured prompts for the LLM to generate syntactically valid and semantically diverse inputs. During execution, I augment traditional coverage-based feedback with semantic feedback signals-derived from program state changes, exception types, and output semantics-allowing the fuzzer to prioritize inputs that trigger novel program behaviors beyond mere code coverage. I implement our approach atop AFL++, combining program instrumentation with embedding-based semantic similarity metrics to guide seed selection. Evaluation on real-world open-source targets, including libpng, tcpdump, and sqlite, demonstrates that our method achieves faster time-to-first-bug, higher semantic diversity, and a competitive number of unique bugs compared to state-of-the-art fuzzers. This work highlights the potential of combining LLM reasoning with semantic-aware feedback to accelerate and deepen vulnerability discovery. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_03995 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hybrid Fuzzing with LLM-Guided Input Mutation and Semantic Feedback Lin, Shiyin Cryptography and Security Artificial Intelligence Software fuzzing has become a cornerstone in automated vulnerability discovery, yet existing mutation strategies often lack semantic awareness, leading to redundant test cases and slow exploration of deep program states. In this work, I present a hybrid fuzzing framework that integrates static and dynamic analysis with Large Language Model (LLM)-guided input mutation and semantic feedback. Static analysis extracts control-flow and data-flow information, which is transformed into structured prompts for the LLM to generate syntactically valid and semantically diverse inputs. During execution, I augment traditional coverage-based feedback with semantic feedback signals-derived from program state changes, exception types, and output semantics-allowing the fuzzer to prioritize inputs that trigger novel program behaviors beyond mere code coverage. I implement our approach atop AFL++, combining program instrumentation with embedding-based semantic similarity metrics to guide seed selection. Evaluation on real-world open-source targets, including libpng, tcpdump, and sqlite, demonstrates that our method achieves faster time-to-first-bug, higher semantic diversity, and a competitive number of unique bugs compared to state-of-the-art fuzzers. This work highlights the potential of combining LLM reasoning with semantic-aware feedback to accelerate and deepen vulnerability discovery. |
| title | Hybrid Fuzzing with LLM-Guided Input Mutation and Semantic Feedback |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2511.03995 |