一作ICML 2026Poster· Reinforcement Learning / Deep RL
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Hao Wang · Ziyi Ni · Huacan Wang · Pin Lyu · Lei Sha
Current defenses for Large Language Models (LLMs) often suffer from a ”memory gap”: parameter-modifying methods are computationally rigid, while inference-time filters cannot retain or reuse defense knowledge across interactions. To address this, we propose SafetyMem, a novel framework that secur...
Hao Wang · Yanting Wang · Hao Li · Rui Li · Lei Sha
Large Language Models (LLMs) have achieved remarkable capabilities but remain vulnerable to adversarial “jailbreak” attacks designed to bypass safety guardrails. Current safety alignment methods depend heavily on static external red teaming, utilizing fixed defense prompts or pre-collected advers...