PBFuzz: Agentic Directed Fuzzing for PoV Generation
Haochen Zeng, Andrew Bao, Jiajun Cheng, Chengyu Song
Proof-of-Vulnerability (PoV) input generation is a critical task in software security and supports downstream applications such as bug reproduction and patch validation. Generating a PoV input requires solving two sets of constraints: (1) reachability constraints for reaching vulnerable code locations, and (2) triggering constraints for activating the target vulnerability. Existing approaches, including directed greybox fuzzing and LLM-assisted fuzzing, struggle to efficiently satisfy these constraints. This work presents an agentic method that mimics human experts. Human analysts iteratively study code to extract semantic reachability and triggering constraints, form hypotheses about PoV triggering strategies, encode them as test inputs, and refine their understanding using debugging feedback. We automate this process with an agentic directed fuzzing framework called PBFuzz. PBFuzz tackles four challenges in agentic PoV generation: autonomous code reasoning for semantic constraint extraction, custom program-analysis tools for targeted inference, persistent memory to avoid hypothesis drift, and property-based testing for efficient constraint solving while preserving input structure. Experiments on the Magma benchmark show strong results. PBFuzz triggered 57 vulnerabilities, surpassing all baselines, and uniquely triggered 17 vulnerabilities not exposed by existing fuzzers. PBFuzz achieved this within a 30-minute budget per target, whereas conventional approaches were allotted 24 hours. Median time-to-exposure was 339 seconds for PBFuzz versus 8,680 seconds for AFL++ with CmpLog, giving a 25.6x efficiency improvement with an API cost of 1.83 USD per vulnerability. In real-world applications, PBFuzz reproduced three FFmpeg 1-day CVEs that had no public PoVs and identified three previously unknown Bento4 vulnerabilities.
尚无 AI 解读，展示原文摘要