PatchIsland: Orchestration of LLM Agents for Continuous Vulnerability Repair
Wonyoung Kim, Seunggi Min, Minjae Gwon, Dowoo Baik, Haein Lee, Hyeon Heo, Minjae Lee, Min Woo Baek, Yonghwi Jin, Younggi Park, Yunjae Choi, Taesoo Kim, Sangdon Park, Insu Yun

TL;DR
PatchIsland is a system that integrates multiple LLM agents into continuous fuzzing pipelines to automate vulnerability repair, significantly improving repair coverage and robustness in noisy, real-world environments.
Contribution
It introduces a novel ensemble of LLM agents and a two-phase deduplication method tailored for continuous fuzzing, enabling effective autonomous vulnerability repair.
Findings
Repaired 84 out of 92 vulnerabilities internally.
Achieved 72.1% repair rate in the AIxCC competition.
Operated fully autonomously without human intervention.
Abstract
Continuous fuzzing platforms such as OSS-Fuzz uncover large numbers of vulnerabilities, yet the subsequent repair process remains largely manual. Unfortunately, existing Automated Vulnerability Repair (AVR) techniques -- including recent LLM-based systems -- are not directly applicable to continuous fuzzing. This is because these systems are designed and evaluated on a static, single-run benchmark setting, making them ill-suited for the diverse, noisy, and failure-prone environments in continuous fuzzing. To address these issues, we introduce PatchIsland, a system for Continuous Vulnerability Repair (CVR) that tightly integrates with continuous fuzzing pipelines. PatchIsland employs an ensemble of diverse LLM agents. By leveraging multiple LLM agents, PatchIsland can cover a wider range of settings (e.g., different projects, bug types, and programming languages) and also improve…
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Taxonomy
TopicsSoftware Testing and Debugging Techniques · Security and Verification in Computing · Software System Performance and Reliability
