Papers by Yakai Li
On the (In-)Security of the Shuffling Defense in the Transformer Secure Inference (2026.acl-long)
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| Challenge: | Existing work reveals only randomly permuted activations to the client, allowing adversaries to extract model weights. |
| Approach: | They propose an attack that aligns differently shuffled activations to a common permutation and exploits them to extract model weights. |
| Outcome: | The proposed attack can align shuffled activations to a common permutation and exploit them to extract model weights with a query cost of approximately $1. |
JailMeter: An Evidence-Based Evaluation Framework for Jailbreak Attacks on Large Language Models (2026.findings-acl)
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Qingjia Huang, Jingyu Zhang, Jianguo Wu, Yakai Li, Weijuan Zhang, Yankai Rong, Junyi Yao, Shengzhi Zhang, Xiaoqi Jia
| Challenge: | Currently, evaluation criteria and methods used for jailbreak effectiveness are inconsistent. |
| Approach: | They propose a framework to measure jailbreak effectiveness using a model that filters out jailbreak noise while preserving the original malicious question. |
| Outcome: | The proposed framework outperforms existing evaluation methods on a challenging benchmark containing 330 human-labeled, non-rejected jailbreak instances. |