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Augmenting Greybox Fuzzing with Generative AI

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arxiv 2306.06782 v1 pith:JNOZVLFB submitted 2023-06-11 cs.CR cs.AI

Jie Hu (University of California Riverside) , Qian Zhang (University of California Riverside) , Heng Yin (University of California Riverside) This is my paper · ORCID
classification cs.CRcs.AI
keywords generativeprogramsapproachfuzzergreyboxmodelssyntaxeffective
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Real-world programs expecting structured inputs often has a format-parsing stage gating the deeper program space. Neither a mutation-based approach nor a generative approach can provide a solution that is effective and scalable. Large language models (LLM) pre-trained with an enormous amount of natural language corpus have proved to be effective for understanding the implicit format syntax and generating format-conforming inputs. In this paper, propose ChatFuzz, a greybox fuzzer augmented by generative AI. More specifically, we pick a seed in the fuzzer's seed pool and prompt ChatGPT generative models to variations, which are more likely to be format-conforming and thus of high quality. We conduct extensive experiments to explore the best practice for harvesting the power of generative LLM models. The experiment results show that our approach improves the edge coverage by 12.77\% over the SOTA greybox fuzzer (AFL++) on 12 target programs from three well-tested benchmarks. As for vulnerability detection, \sys is able to perform similar to or better than AFL++ for programs with explicit syntax rules but not for programs with non-trivial syntax.

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Forward citations

Cited by 10 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Locus: Agentic Predicate Synthesis for Directed Fuzzing

    cs.CR 2025-08 conditional novelty 6.0 of 10

    Locus uses an LLM agent to synthesize and validate intermediate predicates that make directed fuzzing reach target bug states faster, reporting an average 41.6x speedup across eight fuzzers.

  2. Ensemble Fuzzing with Dynamic Resource Scheduling and Multidimensional Seed Evaluation

    cs.SE 2025-07 conditional novelty 6.0 of 10

    Legion dynamically reallocates CPU cores among a set of fuzzers using a multi-armed-bandit scheduler and a five-metric seed evaluation, improving coverage and bug detection over existing ensemble fuzzing.

  3. Directed Greybox Fuzzing via Large Language Model

    cs.CR 2025-05 reject novelty 6.0 of 10

    HGFuzzer uses an LLM to generate harnesses, reachable seeds, and custom mutators for directed greybox fuzzing, and reports triggering 17/20 known vulnerabilities plus finding 9 new ones with CVE IDs.

  4. Harnessing Large Language Models for Seed Generation in Greybox Fuzzing

    cs.CR 2024-11 conditional novelty 6.0 of 10

    SeedMind generates fuzzing seeds by having an LLM write a seed generator and iteratively refine it using code coverage feedback.

  5. CKGFuzzer: LLM-Based Fuzz Driver Generation Enhanced By Code Knowledge Graph

    cs.SE 2024-11 conditional novelty 6.0 of 10

    CKGFuzzer uses a code knowledge graph to guide LLM agents in generating, repairing, and mutating fuzz drivers, reporting a pooled 8.73% relative coverage gain over PromptFuzz on eight libraries and 9 new bugs.

  6. MultiFuzz: A Dense Retrieval-based Multi-Agent System for Network Protocol Fuzzing

    cs.CR 2025-08 reject novelty 4.0 of 10

    MultiFuzz combines retrieval-augmented generation and multiple LLM agents within the ChatAFL protocol fuzzer, reporting marginal and statistically unsupported gains in branch coverage and state exploration for RTSP.

  7. Pixels to Play: A Foundation Model for 3D Gameplay

    cs.CV 2025-08 unverdicted novelty 4.0 of 10

    Pixels2Play-0.1 is a decoder-only transformer trained via behavior cloning and inverse-dynamics-imputed actions to play 3D games from pixels, with only qualitative results reported.

  8. An Agentic Flow for Finite State Machine Extraction using Prompt Chaining

    cs.CL 2025-07 conditional novelty 4.0 of 10

    A three-stage LLM prompt-chaining system extracts FSM rulebooks from RFC documents, achieving F1 scores near 85% on FTP and RTSP.

  9. Retrieval Augmented Generation Based LLM Evaluation For Protocol State Machine Inference With Chain-of-Thought Reasoning

    cs.NI 2025-01 conditional novelty 4.0 of 10

    A RAG-plus-chain-of-thought agent improves BLEU, ROUGE, and WER scores for RTSP packet seed generation over Gemma-2-9B and Llama-3-8B baselines, though the evaluation does not test actual fuzzing effectiveness.

  10. A Contemporary Survey of Large Language Model Assisted Program Analysis

    cs.SE 2025-02 conditional novelty 1.0 of 10

    A review that catalogs how large language models are used in static, dynamic, and hybrid program analysis, and outlines open challenges.

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