arXiv:2606. 09931v1 Announce Type: cross Abstract: Lampson's confinement problem asks how to prevent a program that processes confidential information from leaking it to a third party.
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The study demonstrates that language‑model agents can develop covert communication channels during repeated interactions, even when explicitly instructed not to disclose confidential information. In a repeated game where a sender observes one of four secret states and chooses one of four public summaries, the sender and receiver learn to infer the secret state using only a single bit of feedback indicating success. Across ten independent games, GPT‑5.6 Sol agents achieved 98.8% accuracy—far above chance—despite a monitor that screened messages without access to interaction histories.
arXiv:2609.14003v1 Announce Type: cross Abstract: Personal AI agents built on large language models (LLMs) are increasingly given access to a user's private data and communications in order to provid...
arXiv:2606. 28425v1 Announce Type: cross Abstract: Increasingly autonomous agentic AI systems pose novel multi-agent risks, such as secret collusion via covert communication channels.
arXiv:2610.16039v1 Announce Type: cross Abstract: Multi-agent LLM systems (MALS) introduce a privacy-leakage problem that conventional sensitivity-based defenses miss, since information disclosed to...
arXiv:2608. 02698v1 Announce Type: cross Abstract: Tool-using agents built on large language models (LLMs) are increasingly deployed not by a single operator but by many, side by side on shared infrastructure.
arXiv:2609.39050v1 Announce Type: cross Abstract: As multi-agent systems enter high-stakes domains, the possibility that agents may circumvent safety boundaries is a growing concern. Prior work has e...