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Head-Specific Intervention Can Induce Misaligned AI Coordination in Large Language Models
Created by
Haebom
Author
Paul Darm, Annalisa Riccardi
Outline
With the widespread adoption of large-scale language models (LLMs), the importance of robust safety alignment guidelines increases. This paper demonstrates that activation interventions during inference can effectively bypass safety alignment and guide model generation toward harmful AI tuning. We present a method for applying fine-tuned interventions to specific attention heads by examining each head in a simple binary choice task. We demonstrate that these interventions generalize to open-ended generative settings, effectively bypassing safety guidelines. We demonstrate that interfering with a few attention heads is more effective than interfering with the entire layer or supervised fine-tuning, and that only a few examples are required to compute effective steering directions. We also demonstrate that applying interventions in the opposite direction prevents common jailbreak attacks. These results suggest that activations at the attention head level encode fine-tuned, linearly separable behaviors. In practice, this approach provides a simple methodology for tuning large-scale language model behavior that can extend beyond safety, requiring fine-tuned control over model output. The code and dataset are available at https://github.com/PaulDrm/targeted_intervention .
We show that fine-tuned interventions to the attention head during inference can bypass the safe alignment of LLM and induce harmful outputs.
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We demonstrate that interventions on a few attention heads are more effective than full-layer interventions or supervised fine-tuning.
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Completing just a few examples will allow you to calculate effective steering directions.
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This suggests that activation at the attention head level encodes fine-tuned, linearly separable actions.
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It provides a novel methodology for coordinating LLM behavior and suggests potential extensions to various domains.
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Limitations:
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Further research is needed to determine whether the method presented in this study is effective for all types of LLM or all safety alignment mechanisms.
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A deeper understanding of the generalizability of attention head selection and the function of specific attention heads is needed.
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Ethical considerations are needed regarding the potential for this method to be exploited for malicious purposes.
