ContraFix couples differential runtime evidence from execution variants with reusable repair skills to achieve 84.0% resolution on SEC-Bench and 73.8% on PatchEval using GPT-5-mini, outperforming baselines at lower cost.
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LiveFuzz extends directed greybox fuzzing with abstract path mapping and risk-based mutation to expose library vulnerabilities from client programs on a 61-case dataset, reaching more target paths and triggering three vulnerabilities no baseline found.
citing papers explorer
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ContraFix: Agentic Vulnerability Repair via Differential Runtime Evidence and Skill Reuse
ContraFix couples differential runtime evidence from execution variants with reusable repair skills to achieve 84.0% resolution on SEC-Bench and 73.8% on PatchEval using GPT-5-mini, outperforming baselines at lower cost.
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Triggering and Detecting Exploitable Library Vulnerability from the Client by Directed Greybox Fuzzing
LiveFuzz extends directed greybox fuzzing with abstract path mapping and risk-based mutation to expose library vulnerabilities from client programs on a 61-case dataset, reaching more target paths and triggering three vulnerabilities no baseline found.