VulAgent: Hypothesis-Validation Driven Multi-Agent Architecture for Vulnerability Detection

Vulnerability detection with language models is challenging: it requires (i) precisely localizing security-sensitive code and (ii) reasoning about potential vulnerability conditions under complex, partially observed program context. We present VulAgent, a multi-agent vulnerability detection framework based on hypothesis validation. Our design is inspired by how human auditors review code: when noticing a sensitive operation, they form a hypothesis about a possible vulnerability, consider potential trigger paths, and then verify the hypothesis against the project context. Given a code unit, VulAgent first applies multi-view analyzers to identify and localize security-sensitive operations from complementary perspectives. For each sensitive operation, it then constructs an explicit vulnerability hypothesis-including triggering (or exploitation) preconditions and a candidate trigger path-and validates the hypothesis using project context together with the model's general knowledge of commonly used APIs and security patterns. This validation-oriented design reduces speculative reports and substantially lowers false positives. Across PrimeVul and SVEN, VulAgent improves accuracy by 6.6 percentage points on average, increases vulnerable-fixed pair identification by up to 4.5× (2.46× on average), and reduces false positive rate by 36% relative to recent LLMbased baselines.