LLM-based coding agents increasingly rely on external program knowledge, yet access to static-analysis graphs alone does not ensure appropriate operation selection or evidence interpretation. We present APG, an infrastructure for semantics-grounded static program analysis. Built on SVF and LLVM, it decomposes compiler-...
Ze-Xin Zhong, Yan-Jun Zhang, Yu-Lei Sui et al.· 0 citations
Watermarking LLM-generated text is an important task for tracing its provenance. Existing LLM watermarks preserve provenance under editing, but this same robustness allows an adversary to alter critical content while retaining attribution, a vulnerability known as piggyback spoofing. We introduce an innovative watermar...
Xiao-Yang Feng, Yanjun Zhang, He Zhang et al.· 0 citations
This work proposes IdentityMask, a robust protection framework that shifts the paradigm from arbitrary confusion to precise, targeted feature corruption, and consistently outperforms prior state-of-the-art approaches in both protection efficacy and robustness.
Wei-Wei Tan, Rui Wang, Lihua Jing et al.· Proceedings of the Thirty-Fi...· 0 citations
Gradient-seeded Reinforcement Learning And Stealthy Pruning (GRASP), a three-stage framework that tackles challenges of adversarial attacks on machine learning-based malware detectors, and out-performs baselines, achieving higher attack success with fewer queries and smaller file-size inflation.
Yu-Tong Liu, Jian-Ting Ning, Qi Feng et al.· Proceedings of the Thirty-Fi...· 1 citation
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