From Risk Classification to Action Plan Remediation: A Guardrail Feedback Driven Framework for LLM Agents

LLM-based guardrails typically safeguard agents by evaluating proposed actions or inputs before execution, producing safety signals such as binary allow/deny decisions, risk categories, and/or explanatory rationales about potential policy violations. However, agent risks often arise when otherwise benign tasks are contaminated by untrusted external content, unsafe instructions, or risky tool use. Existing guardrails often flag the entire task uniformly as unsafe, thereby blocking the threat but

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ethics.ai (4 June 2026), “From Risk Classification to Action Plan Remediation: A Guardrail Feedback Driven Framework for LLM Agents,” evidence record 1444, https://ethics.ai/record/1444 (originally published by arXiv).

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