Safeguarding LLM Agents from Misalignment through Provenance Analysis
As LLM agents gain increasing access to powerful tools, ensuring that their actions are aligned with the user's intent becomes critical. When an agent's proposed tool invocation deviates from the user's intent -- a phenomenon called misalignment -- it may lead to harmful consequences that are difficult to undo. Existing runtime guardrails rely on an LLM-as-a-judge paradigm that lacks a systematic framework for reasoning about alignment, often producing judgments that are inconsistent or difficul
Record details
Published: 1 May 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Agents & autonomy
Retrieved: 14 July 2026
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ethics.ai (1 May 2026), “Safeguarding LLM Agents from Misalignment through Provenance Analysis,” evidence record 5134, https://ethics.ai/record/5134 (originally published by arXiv).
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