arXiv AI
Deny Without Disabling: Authorization-Paired Evaluation and Control for Multi-Agent Systems
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Agentic Permissions Policy Algebra for Taint Confinement in LLM Agents
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arXiv AI
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When Tool Outputs Become Commands: Separating Action Induction from Runtime Authorization in Tool-Augmented LLM Agents
The paper introduces SARA, a framework that separates action induction from runtime authorization in tool‑augmented LLM agents. By treating these as distinct roles, SARA uses an Action Probe to record action provenance and only authorizes tool calls that align with the user objective and past successful executions. Experiments on AgentDojo and AgentDyn show that SARA reduces action‑to‑side‑effect risk to below 0.63% while preserving task performance.
arXiv AI
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ChainCaps: Composition-Safe Tool-Using Agents via Monotonic Capability Attenuation
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CoSec: Benchmarking Agent Security in Communities
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arXiv AI
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Confuse the Model, Control the Flow: Understanding and Mitigating Privacy Leakage from LLM Agents with Information Flow Control
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