Safeguarding Language Models via Self-Destruct Trapdoor

Le résumé fourni par la source

The potential misuse and misalignment of language models (LMs) is a central safety concern. This work presents Self-Destruct, a novel mechanism to restrict specific behaviors in LMs by leveraging overlooked properties of the underlying hardware. We observe that the LM frameworks use limited-precision formats (e.g., FP32), which are vulnerable to overflow errors during matrix multiplications. Exploiting this property, Self-Destruct replaces selected weights in pre-trained LM layers with values that act as traps, triggering a system error only when the model engages in targeted behaviors, such as harmful text generation, while leaving normal functionality unaffected. Unlike post-hoc filters, this safeguard is embedded directly within the model, introduces neither inference overhead nor auxiliary models, and requires only a set of examples for calibration. Experiments on Llama-3.2 and Qwen-2.5 demonstrate that Self-Destruct provides competitive protection against jailbreak attacks while preserving accuracy on standard benchmarks. Our results highlight the potential of hardware-aware safeguards as an efficient, low-overhead complement to existing LM defenses.

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