AI-Guided Fuzzing
AI-guided fuzzing uses large language models and machine learning to assist in generating fuzzing targets, writing invariant properties, and directing fuzzing campaigns.
In Depth
AI-guided fuzzing combines traditional fuzzing techniques with LLM capabilities to speed up the testing process. LLMs can analyze smart contract code and suggest invariant properties, generate test harnesses, identify likely vulnerability patterns, and prioritize code paths for deeper testing. However, LLM-generated properties need human validation — they can hallucinate non-existent functions, miss protocol-specific constraints, or write properties that are technically correct but trivially true. The most effective approach uses AI as an accelerator with human-in-the-loop review. See our practical guide to AI-guided fuzzing and assessment of LLM property generation.
Frequently Asked Questions
What is AI-guided fuzzing?
AI-guided fuzzing uses large language models to assist with fuzzing workflows — generating invariant property suggestions, creating test harnesses, and identifying promising code paths. The AI accelerates human work but requires validation, since LLMs can hallucinate incorrect properties.
Can AI replace human auditors for fuzzing?
Not yet. AI-generated properties have significant hallucination rates for complex DeFi logic. The best workflow uses AI to generate candidates that humans review and refine before running with actual fuzzers. See our honest assessment of what works and what doesn't.