Agentic Permissions Policy Algebra for Taint Confinement in LLM Agents

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Computer Science > Cryptography and Security

arXiv:2607.24625v1 (cs)

[Submitted on 27 Jul 2026]

Title: Agentic Permissions Policy Algebra for Taint Confinement in LLM Agents

Authors: Arseny Kravchenko , Vadim Liventsev , Innokentii Konstantinov , Ildar Iskhakov , Matvey Kukuy

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Abstract: 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 branching and prospective acquisition enforcement. Before data acquisition occurs, APPA prospectively evaluates label descents and missing prerequisites, generating actionable remedy plans (Authorize, Accept). To inspect unvetted data without polluting the primary context, a label-seeded child trajectory is spawned, absorbing label descent locally and allowing a trusted sanitizer to return a bounded derivative to the unchan

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