arXiv:2605.31073v1 Announce Type: new Abstract: Reasoning-based LLM guardrails improve safety moderation by generating explicit rationales before issuing final decisions. However, their rationales do not always lead to faithful enforcement: a model may recognize a harmful intent in its reasoning but still predict a safe label, or issue an unsafe decision without policy-grounded justification. We identify this safety-critical failure mode as the deliberation-to-enforcement gap. Unlike general chain-of-thought faithfulness, guardrail reliability requires policy execution consistency: the generat
The rapid deployment and increasing autonomy of LLMs necessitate robust and reliable safety mechanisms, making granular guardrail faithfulness a critical and immediate research focus.
Guardrail reliability, by ensuring LLMs adhere to intended safety policies, directly impacts trust, regulatory acceptance, and the safe deployment of increasingly sophisticated AI systems across all sectors.
The focus is shifting from general safety moderation to ensuring the consistency and faithfulness of LLM guardrails in translating 'deliberation' (reasoning) into 'enforcement' (decisions).
- · AI developers
- · LLM safety researchers
- · Enterprises deploying LLMs
- · Regulators
- · Users encountering unfaithful LLM responses
- · AI systems lacking transparent safety mechanisms
Improved safety and reliability of LLM deployments due to more consistent guardrail enforcement.
Increased public and institutional trust in AI systems, potentially accelerating their integration into sensitive applications.
Enhanced regulatory confidence, possibly leading to more streamlined adoption pathways for compliant AI technologies.
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Read at arXiv cs.CL