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# Refusal Subspace

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  1. arXiv

    Prefill jailbreaks meet a training-free fix: restore the refusal signal the attack erodes, request by request

    Prefill jailbreaks force an unsafe opening into the assistant turn so a model that would refuse continues instead. Tracking vocabulary-grounded signals with the Jacobian lens shows refusal weakening faster than harmfulness as the forced prefix deepens, reaching the benign baseline by free generation. J-Anchor, a training-free inference-time defense, anchors each request to its pre-attack assistant-header state and restores only the refusal components that fall below that target during prefilling and decoding, cutting attack success on fixed and deep prefills across four open-weight instruction models while keeping benign refusal and reasoning close to the undefended model.

  2. arXiv

    Needle removes LLM backdoors with one weight-orthogonalisation edit, cutting code-injection attack success to zero while keeping capability and safety loss lowest

    The work proposes Needle, a training-free backdoor removal method: given an identified trigger, it estimates a backdoor direction and a refusal subspace from activation vectors, then applies sequential weight orthogonalisation, achieving the lowest mean attack success rate among the evaluated defences across Gemma-3-4B-IT, Qwen3-4B-Instruct-2507 and Gemma-3-12B-IT, fully removing a code-injection attack while producing the lowest KL divergence and minimal capability and safety change.

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