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Yue-Kang Li

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Book Open access Oct 2026

SAF: An AI-Agent-Ready and Browser-Accessible Static Analysis Framework for LLVM IR

Static program analysis finds bugs before software ships, and modern checkers for C/C++ depend on whole-program infrastructure: pointer analysis, value-flow tracking, taint analysis, and IFDS-based data flow on top of LLVM. Today’s LLVM-IR frameworks fall short. SVF, Phasar, and Lotus expose their analyses through C++...

Yue-Kang Li, Wei Li, Wei Song et al. · 0 citations
#artificial intelligence Preprint Oct 2026

One Skill Too Many: How Co-Installed Skills Conflict in Coding Agents

Coding agents are extended with agent skills, directories whose SKILL.md tells the model when and how to perform a task. Because skills come from independent sources (teams, developers, plugins, copied collections), an installed skill can be co-installed with a similar skill doing the same job, and the model picks betw...

Chao-Liang Yan, Zi-Hao Xu, Yue-Kang Li et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Just Ask Jev: Reinforcement Learning for Calibrated Decisions as a Zero-Shot Detector of AI Alignment Failures

RLCDAlignBench is presented, which benchmarks Jev on ten alignment failures: sycophancy, jailbreaks, deception, prompt injection, hallucination, privacy violation, social bias, reward hacking, concealing uncertainty, and power seeking.

Ruo-Qi Guo, Yi Liu, Ge-Lei Deng et al. · 6 citations
Preprint Aug 2026

Decoupling is a Necessity: Transformation-Agnostic Decompiled Code Recovery under Optimization and Obfuscation

ReSource is presented, the first multi-phase LLM framework designed for transformation-agnostic source recovery, which maintains robust semantic identifiability where state-of-the-art baselines severely overfit or degrade, provides a scalable and reliable foundation for downstream security analysis.

Zhi-Ping Zhou, Xiaohong Li, Ruitao Feng et al. · 0 citations
Preprint Sep 2026

Do Input-Level Defenses Transfer to Observation-Level Attacks on VideoLLMs?

DefTEval is introduced, a controlled evaluation framework that systematically assesses whether input-level adversarial defenses, which operate on the pixel content of already-sampled frames, can mitigate observation-level attacks and demonstrates that securing VideoLLMs requires system-level robustness mechanisms spann...

Bang-Shuo Zhu, Wei Song, Yu-Xin Cao et al. · 0 citations

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