arXiv:2606.25750v1 Announce Type: cross Abstract: Safety evaluation of large language models (LLMs) is commonly performed by querying models with unsafe or jailbreak prompts and judging whether their outputs violate a safety policy. Although useful, output-level evaluation is expensive, sensitive to judge choice, and easily tied to fixed question banks. We propose **SafeVec**, a white-box evaluation procedure that measures safety from internal representations rather than generated answers. **SafeVec** first extracts layer-wise refusal directions from a safety-aligned reference model, then sele
The rapid deployment and increasing sophistication of large language models are amplifying concerns about safety and alignment, necessitating more efficient and robust evaluation methodologies.
This new methodology, SafeVec, offers a white-box approach to LLM safety, potentially overcoming limitations of output-level evaluations and enabling proactive safety measures.
Safety evaluation of LLMs could shift from reactive, output-based assessments to proactive, internal representation-based analysis, improving scalability and reliability of safety checks.
- · AI developers
- · Safety researchers
- · LLM evaluators
- · Malicious actors exploiting LLMs
- · Companies with opaque LLM safety practices
Improved detection of safety violations and reduced jailbreaking susceptibility in LLMs.
Faster iteration cycles for safety alignment, leading to more trustworthy and deployable AI systems across various applications.
Potential for an industry standard in white-box safety evaluation, fostering greater transparency and accountability in AI development.
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Read at arXiv cs.LG