arXiv:2606.12342v1 Announce Type: new Abstract: Domain fine-tuning degrades the safety of large language models: fine-tuned specialists readily comply with harmful prompts framed in domain language. Existing inference-time defenses that mix logits from a safe anchor model require both models to share a vocabulary, which rules them out for the cross-family specialists where safety is most degraded. We present ALIGNBEAM, a training-free method that lifts this restriction by translating anchor logits into the target model's vocabulary token-by-token at each decoding step; a small LLM judge then s
The proliferation of specialized large language models and their potential for misuse necessitates immediate solutions for maintaining safety controls without hindering domain-specific performance.
This development addresses a critical vulnerability in LLM deployment, enabling safer application of powerful AI in sensitive domains, and mitigating reputational and regulatory risks.
It introduces a training-free method to transfer safety alignments across disparate LLM architectures, significantly broadening the applicability of existing safety measures.
- · AI developers
- · Domain-specific AI applications
- · Organizations deploying LLMs
- · Actors exploiting LLM safety vulnerabilities
- · Companies relying on outdated safety mechanisms
Specialized LLMs can now be deployed with enhanced safety, reducing the risk of harmful outputs in domain-specific contexts.
The ability to mix logits across different model vocabularies could accelerate the development of more robust and diverse AI safety and control mechanisms.
This innovation may contribute to increased public trust in AI applications, potentially easing regulatory friction for broader LLM adoption across industries.
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