Graph Is the Verifier: Agentic Reinforcement Learning for Interprocedural Vulnerability Detection
Real-world vulnerabilities often span multiple functions, yet most learning-based detectors classify each function in isolation: on a sample of real CVEs, we find that 71.7% of vulnerable functions require evidence from outside the function to be classified correctly. Agentic reinforcement learning (RL) could close this gap by enabling a model to gather that evidence itself, but it lacks a reliable reward, since a reward defined on the final verdict alone can be obtained without performing any investigation. We propose VulAgentRL, an agentic RL framework for interprocedural vulnerability detection built on a Code Property Graph (CPG). The CPG serves two roles: at inference time the policy...
| Authors | Yikun Li, Ting Zhang, Jiakun Liu et al. |
|---|---|
| Primary category | cs.CR |
| Published | 29 July 2026 |
| Last updated at source | 29 July 2026 |
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