Latent-space Attacks for Refusal Evasion in Language Models
Safety-aligned language models are trained to refuse harmful requests, yet refusal behavior can be suppressed by steering their internal representations. Existing methods do so by ablating a refusal direction from model activations, aiming to remove refusal from the model's residual stream. Despite their empirical success, these methods lack a principled account of the latent-space transformation they induce and why it suppresses refusal. In this work, we recast refusal suppression as a latent-space evasion attack against linear probes trained to separate refused from answered prompts. Under this view, prior work's difference-in-means direction naturally defines such a probe, and its abla...
| Authors | Giorgio Piras, Raffaele Mura, Fabio Brau et al. |
|---|---|
| Primary category | cs.AI |
| Published | 20 May 2026 |
| Last updated at source | 5 June 2026 |
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