Contextualizing Sink Knowledge for Java Vulnerability Discovery

• URL: http://arxiv.org/abs/2604.01645v2
• Date: Tue, 07 Apr 2026 04:31:12 GMT
• Title: Contextualizing Sink Knowledge for Java Vulnerability Discovery
• Abstract summary: GONDAR is a sink-centric fuzzing framework that systematically leverages sink API semantics for targeted vulnerability discovery.<n>GONDAR first identifies reachable and exploitable sink call sites through CWE-specific scanning.<n>It then deploys two specialized agents that work collaboratively with a coverage-guided fuzzer.
• Score: 7.342448037592857
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Java applications are prone to vulnerabilities stemming from the insecure use of security-sensitive APIs, such as file operations enabling path traversal or deserialization routines allowing remote code execution. These sink APIs encode critical information for vulnerability discovery: the program-specific constraints required to reach them and the exploitation conditions necessary to trigger security flaws. Despite this, existing fuzzers largely overlook such vulnerability-specific knowledge, limiting their effectiveness. We present GONDAR, a sink-centric fuzzing framework that systematically leverages sink API semantics for targeted vulnerability discovery. GONDAR first identifies reachable and exploitable sink call sites through CWE-specific scanning combined with LLM-assisted static filtering. It then deploys two specialized agents that work collaboratively with a coverage-guided fuzzer: an exploration agent generates inputs to reach target call sites by iteratively solving path constraints, while an exploitation agent synthesizes proof-of-concept exploits by reasoning about and satisfying vulnerability-triggering conditions. The agents and fuzzer continuously exchange seeds and runtime feedback, complementing each other. We evaluated GONDAR on real-world Java benchmarks, where it discovers four times more vulnerabilities than Jazzer, the state-of-the-art Java fuzzer. Notably, GONDAR also demonstrated strong performance in the DARPA AI Cyber Challenge, and is integrated into OSS-CRS, a sandbox project in The Linux Foundation's OpenSSF, to improve the security of open-source software.

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