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cs.CL2026
Defending Against Malicious Finetuning by Scaling Train-time Adversarial Attacks
Haoming Wen, Shi Chen, Qingyu Shi +4
Malicious finetuning attacks pose a major safety threat against open-weight large language models (LLMs). However, existing alignment-stage defenses provide limited protection agai…
cs.CL2025
Demonstrations of Integrity Attacks in Multi-Agent Systems
Can Zheng, Yuhan Cao, Xiaoning Dong +1
Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language understanding, code generation, and complex planning. Simultaneously, Multi-Agent Systems…
cs.CL2025
Can LLMs Generate Reliable Test Case Generators? A Study on Competition-Level Programming Problems
Yuhan Cao, Zian Chen, Kun Quan +17
Large Language Models (LLMs) have demonstrated remarkable capabilities in code generation, capable of tackling complex tasks during inference. However, the extent to which LLMs can…