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cs.CR2026
RAS: Measuring LLM Safety Through Refusal Alignment
Chang-Chieh Huang, Yan-Lun Chen, Chia-Mu Yu +1
Safety evaluation of large language models (LLMs) is commonly performed by querying models with unsafe or jailbreak prompts and judging whether their outputs violate a safety polic…
cs.CR2026
Tracing Target Answers in Poisoned Retrieval Corpora via Token Influence Attribution
Yan-Lun Chen, Pin-Yu Chen, Chia-Mu Yu +3
Retrieval-Augmented Generation (RAG) systems are vulnerable to corpus poisoning attacks that manipulate model outputs through malicious retrieved documents. Existing detection meth…
cs.CR2026
CodeSentinel: A Three-Layer Defense Against Indirect Prompt Injection in Code Contexts
Po-Han Cheng, Chia-Mu Yu, Ying-Dar Lin +2
Code large language models increasingly retrieve external code context from repositories, documentation, issue threads, and coding-agent environments, creating an indirect prompt-i…