arXiv:2606.08044v1 Announce Type: new Abstract: Large Language Model (LLM) safety has often been evaluated at the behavior level, which provides limited evidence of internal robustness, as these evaluations target outputs rather than representation-level vulnerability under intervention. We formalize this discrepancy as the audit gap: the difference between behavioral safety and robustness under intervention. To study this gap, we construct dissociated models that preserve safe outward behavior while remaining vulnerable in the latent space. We introduce an intervention-based evaluation framew
The rapid deployment and increasing capabilities of Large Language Models necessitate more robust and nuanced safety evaluation methods beyond superficial behavioral assessments.
This research provides a more sophisticated framework for evaluating the deep-seated safety and robustness of LLMs, moving beyond easily manipulated surface-level behaviors to internal vulnerabilities.
The focus of LLM safety evaluations will shift from merely observing outputs to probing the internal representations and underlying robustness of models, potentially leading to more secure and trustworthy AI systems.
- · AI safety researchers
- · Developers of secure AI systems
- · Regulatory bodies
- · Developers relying solely on behavioral safety evaluations
- · Models with latent vulnerabilities
This research will lead to the development of new tools and methodologies for auditing the internal safety of LLMs.
AI developers will need to adopt these more rigorous evaluation techniques, increasing the complexity and cost of model development but improving trustworthiness.
The enhanced scrutiny of LLM internals could pave the way for more explainable AI, as understanding latent vulnerabilities requires insight into internal mechanisms.
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Read at arXiv cs.LG