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Evidence record 14034 · automatically gathered

Agentic Permissions Policy Algebra for Taint Confinement in LLM Agents

Autonomous LLM agents processing mixed-confidentiality data face severe security risks from prompt injection attacks and reasoning errors. While dynamic Information Flow Control (IFC) provides structural security guarantees, traditional taint tracking permanently taints an agent's context upon reading unvetted data, severely restricting downstream utility. We present APPA (Agentic Permissions Policy Algebra), an IFC framework that resolves this usability bottleneck through engine-managed context

Record details

Published: 27 July 2026
Source: arXiv cs.AI
Category: Research
Topics: Regulation · Privacy · Agents & autonomy
Retrieved: 28 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (27 July 2026), “Agentic Permissions Policy Algebra for Taint Confinement in LLM Agents,” evidence record 14034, https://ethics.ai/record/14034 (originally published by arXiv cs.AI).

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