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cs.AI2026
Emergent Misalignment Is Not Magical
Mingxuan Li, Qirun Dai, Heran Wang +1
Fine-tuning large language models (LLMs) on narrowly harmful datasets can lead to misalignment broadly, a phenomenon known as emergent misalignment (EM). EM poses a challenge for A…
cs.SD2026
Never Stop Speaking: a Denial-of-Service Attack on End-to-End Speech Language Models
Shuozhe Cheng, Kunlan Xiang, Mingxuan Li +3
Many studies have shown that specially crafted inputs can induce large language models (LLMs) to generate excessively long outputs, resulting in significant computational overhead…
cs.CR2026
When Poison Fails After Retrieval: Revisiting Corpus Poisoning under Chunking and Reranking Pipelines
Xi Nie, Hongwei Li, Shenghao Wu +3
Retrieval-Augmented Generation (RAG) systems are vulnerable to corpus poisoning attacks that manipulate downstream model outputs through malicious knowledge injection. Existing stu…