arXiv AI September 30, 2026 By Benoit Dherin, Michael Munn, Xavier Gonzalvo, Adrian Goldwaser, Blaz Bratanic, Ananth Balashankar, Andrey Vlasov, Pinzhi Huang, Cecile Loge, Nicole Mitchell, Andre Fernandes, Trilok Acharya, Wendy Kan, Ziyue Wang, Hanna Mazzawi, Felipe Tiengo Ferreira, Mor Geva

The Safety Operator: Modulating the Expression of Safety Instructions via Spectral Optimization

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arXiv Computation and Language

Oct 1

The System Prompt Illusion: How Instruction Preambles Modify Computation in Language Models

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arXiv Machine Learning

Jul 14

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arXiv AI

Jul 20

Latent Fusion Jailbreak: Blending Harmful and Harmless Representations to Elicit Unsafe LLM Outputs

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llms multimodal benchmarks safety

arXiv Computation and Language

Aug 25

Safety-Aligned Weights Are Not Enough: Refusal-Teacher-Guided Finetuning Enhances Safety and Downstream Performance under Harmful Finetuning Attacks

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llms fine-tuning safety

Hugging Face Trending Papers

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Safety Paradox: How Enhanced Safety Awareness Leaves LLMs Vulnerable to Posterior Attack

Large language models (LLMs) are rigorously aligned to refuse harmful requests, a process that inherently cultivates a latent capacity to evaluate and recognize unsafe content. In this work, we reveal that this advanced safety awareness inadvertently introduces a fatal vulnerability.

llms reinforcement-learning safety

Hugging Face Trending Papers

Jul 2

Safety Targeted Embedding Exploit via Refinement

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