OPIUM: Mitigating Steering Externalities and Over-Refusal via Dual Objective Latent Optimization

Abstract:Activation steering provides a lightweight mechanism for controlling large language models at inference time, but steering vectors can have unintended externalities: utility vectors may weaken safety behavior, while refusal vectors may induce over-refusal on benign prompts. We introduce OPIUM (Optimizing Protected Injections via Utility Manifolds), a training-free method for sanitizing steering vectors through representation matching. Given reference behaviors on two prompt sets, OPIUM optimizes a new steering vector that preserves the downstream representations induced by the desired intervention while matching a safer reference behavior on prompts where the original vector fails. Across steering-externality and over-refusal settings, OPIUM improves the safety--utility tradeoff relative to vanilla steering and directional ablation, suggesting that harmful side effects of activation steering can often be mitigated directly in activation space.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2607.19806 [cs.LG]

(or arXiv:2607.19806v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.19806 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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