Papers by Fanhua Shang

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Challenge:	Large Language Models (LLMs) remain vulnerable to jailbreaking attacks despite advances in safety alignment .
Approach:	They propose a new diagnostic auditing framework that dismantles the model's internal safety anchor by precisely scaling attention scores in these vulnerable layers.
Outcome:	The proposed framework achieves a state-of-the-art Attack Success Rate of over 77% with an unprecedented efficiency of 1.53 queries on average.