arXiv:2605.21706v2 Announce Type: replace Abstract: 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 traine
The rapid deployment of safety-aligned language models has created an immediate need for understanding and mitigating methods that bypass their intended refusal behaviors.
This work introduces a principled framework for understanding and executing latent-space attacks on language models, directly impacting AI safety, security, and the reliability of AI systems in sensitive applications.
The ability to systematically evade refusal behaviors through latent-space manipulation means that current safety alignment techniques are vulnerable to more sophisticated and less detectable attacks.
- · Malicious actors
- · AI red teamers
- · AI researchers focusing on adversarial robustness
- · AI safety researchers (current paradigm)
- · Organizations relying solely on current refusal mechanisms
Refusal-aligned language models become less reliable for critical applications without enhanced defenses.
Increased investment in proactive adversarial training and more robust safety alignment techniques will be required to counter these evasion methods.
The arms race between AI safety and evasion techniques could lead to more complex and opaque AI system architectures, making auditing and interpretability even more challenging.
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Read at arXiv cs.AI