Internal safety scores judge a prompt before any text is generated, and they are validated by how well they separate harmful prompts from benign ones. That separation is then read as evidence that the score will also catch the attacks that succeed. Harmful intent is a property of the prompt. Jailbreak success is an out...
Ming-Yu Luo, Ming Deng, Zilang Qiu et al.· 0 citations
Many analytics tasks require secure computation over encrypted data. In particular, fine-grained data such as pixel-level images require higher precision, as every pixel can directly affect outcomes in tasks like tumor segmentation and anomaly detection. While Multi-Party Computation (MPC) is interactive, Differential...
Chao Wang, Shu-Bing Yang, Xiao-Yan Sun et al.· 0 citations
ActProbe, an internal-state-based framework for detecting and localizing poisoned segments in multi-source LLM inputs, is proposed and remains effective against defense-aware adaptive attacks and can protect black-box APIs through surrogate-based poisoned-segment removal.
Xue Tan, Chang-Hui Wang, Sanrui Yang et al.· 0 citations
AcMAS is proposed, an activation-based framework for malicious-behavior detection in MAS that significantly outperforms graph-based baselines against stealthy attacks, with generalization across diverse open-source LLM backbones, attack intensity, and MAS scale.
Haowen Xu, Xue Tan, Lei Ma et al.· arXiv.org· 0 citations
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