ニュース
Agentic Permissions Policy Algebra for Taint Confinement in LLM Agents
arXiv cs.AI · 公開日 · 読了3分
30秒で要点
- 何が起きたか
- Researchers presented APPA, an information flow control framework that lets LLM agents safely inspect untrusted data without permanently tainting their working context.
- なぜ重要か
- Engineers building autonomous agents that handle mixed-confidentiality data and need to balance security against prompt injection with practical utility.
- 注意点
- Framework is evaluated on four models with varying results; three showed utility recovery but one did not, suggesting generalization remains an open question.
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記事より
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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
View a PDF of the paper titled Agentic Permissions Policy Algebra for Taint Confinement in LLM Agents, by Arseny Kravchenko and 4 other authors
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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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- agent
- agentic
- llm
- reasoning
- prompt
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