Code Language Models (CLMs) are increasingly adopted for vulnerability detection, yet knowing when to trust their predictions remains an open challenge. Uncertainty estimation has improved reliability in traditional discriminative models, but whether these benefits transfer to the generative paradigm of CLMs is unclear...
Hao-Dong Li, Xiao Cheng, Xu-Dong Wang et al.· Proceedings of the 41st IEEE...· 0 citations
Precise analysis of multi-threaded programs requires combining flow-sensitive pointer analysis (FSPTA) with interleaving and lock analysis (ILA) to reason about cross-thread value flows under feasible concurrent executions. ILA computes may-happen-in-parallel (MHP) relations and lock-release spans to determine when sha...
Jia-Wei Yang, Xiao Cheng, Jia-Wei Wang et al.· 0 citations
BenchShield is presented, a model-backed instrumentation layer for reward integrity in LLM-agent evaluation that grounds detection in a finite lifecycle model of an evaluation's reward-relevant events and achieves 96% accuracy in detecting reward hacking from infrastructure-side evidence.
Sheng-Han Zheng, Zong-Lin Di, Yimin Liu et al.· 0 citations
LeanGuard is presented, a neuro-symbolic framework that assigns each act to the side equipped for it, and it is argued that the remedy is not better prompting but a separation of roles: the component that interprets the code must not also be the one that decides a safety obligation is met.
Yanjie Zhao, Hongjie Chen, Li Lu et al.· arXiv.org· 0 citations
The evaluation shows that AgentFlow recovers richer agent entities and dependencies than existing AST-based agent static analysis tools, generates more dependency-aware Agent BOMs, and uncovers 238 taint-style prompt-to-tool risks in real-world agent programs.
Shenao Wang, Xinyi Hou, Yanjie Zhao et al.· 0 citations
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