arXiv AI
Confuse the Model, Control the Flow: Understanding and Mitigating Privacy Leakage from LLM Agents with Information Flow Control
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arXiv:2606. 26627v1 Announce Type: cross Abstract: Large language model agents increasingly query databases, search document collections, call external APIs, remember past interactions, and act on a user's behalf.
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Agentic Permissions Policy Algebra for Taint Confinement in LLM Agents
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Hugging Face Trending Papers
Aug 3
MNC: Scope-Bound Semantic Declassification for Private LLM-Agent Communication
Multi-agent large language model (LLM) systems can expose protected state through internal messages, tool arguments, logs, and persistent memory even when their public outputs appear innocuous. Existing privacy prompts, redaction methods, and source-level access controls restrict surface content or data access, but do not specify what a legitimately informed agent should disclose or how that disclosure may be reused downstream.
arXiv AI
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The LLMbda Calculus: AI Agents, Conversations, and Information Flow
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