Yuan Zhou

This paper conducts a large-scale empirical study demonstrating that Java library exploits can accurately identify affected versions, achieving high recall and precision, and proposes strategies for exploit migration to improve coverage.

The paper analyzes protracted vulnerabilities (PCVEs) in open-source projects and proposes DeeptraVul, an enhanced detection approach that significantly improves vulnerability coverage by integrating multiple development artifacts and an LLM.

This paper introduces a novel backdoor attack (ACB) against Reinforcement Learning with Verifiable Rewards (RLVR), demonstrating that poisoning the training data can implant a backdoor that significantly degrades the LLM's safety performance.