mlsanityData to 14 Oct 2026

A Note on the Strategic Confinement Problem

arXiv 2606.09931 ↗First posted 7 Jun 2026cs.GT cs.AIcited by 1

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Abstract (arXiv)

Lampson's confinement problem asks how to prevent a program that processes confidential information from leaking it to a third party. We introduce the strategic confinement problem, which arises when the communicating parties are strategic agents with shared coordination resources. In this setting, residual communication capacity can be concentrated on low-entropy, high-impact predicates of the confidential data. Consequently, bounds on information leakage need not induce corresponding bounds on worst-case harm: a channel with negligible capacity may still suffice to select damaging outcomes. We argue that systems of learnt strategic agents naturally instantiate this problem because they do not admit complete behavioural specifications, their learnt conventions generally cannot be predicted or reproduced by an external observer, and sufficiently capable agents can construct covert communication schemes that are difficult to detect or eliminate. Our contribution is therefore not a new theory of communication, but a reinterpretation of confinement in the presence of strategic agents. Classical confinement bounds what information may flow; strategic confinement highlights that this need not bound what strategic agents can jointly achieve.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 23 of its 50 references that are in the map. First posted on arXiv 7 Jun 2026.

Builds on

23 of 50 references.

References · 23

  1. 2017Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networkscited by 744
  2. 2025Monitoring Reasoning Models for Misbehavior and the Risks of Promoting Obfuscationcited by 384
  3. 2022The Effects of Reward Misspecification: Mapping and Mitigating Misaligned Modelscited by 328
  4. 2016Cooperative Inverse Reinforcement Learningcited by 300
  5. 2025Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safetycited by 249
  6. 2023Undetectable Watermarks for Language Modelscited by 226
  7. 2025Multi-Agent Risks from Advanced AIcited by 163
  8. 2024Emergent social conventions and collective bias in LLM populationscited by 123
  9. 2024Secret Collusion among AI Agents: Multi-Agent Deception via Steganographycited by 113
  10. 2025Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agentscited by 71
  11. 2023LLM-Coordination: Evaluating and Analyzing Multi-agent Coordination Abilities in Large Language Modelscited by 68
  12. 2023Preventing Language Models From Hiding Their Reasoningcited by 65
11 more
  1. 2024Hidden in Plain Text: Emergence & Mitigation of Steganographic Collusion in LLMscited by 55
  2. 2022Perfectly Secure Steganography Using Minimum Entropy Couplingcited by 44
  3. 2025Infrastructure for AI Agentscited by 41
  4. 2025Agent Name Service (ANS): A Universal Directory for Secure AI Agent Discovery and Interoperabilitycited by 23
  5. 2022Illusory Attacks: Information-Theoretic Detectability Matters in Adversarial Attackscited by 12
  6. 2026A Decision-Theoretic Formalisation of Steganography With Applications to LLM Monitoringcited by 8
  7. 2026Generative AI collective behavior needs an interactionist paradigmcited by 6
  8. 2026Tacit Coordination of Large Language Modelscited by 5
  9. 2020Information Leakage Games: Exploring Information as a Utility Functioncited by 2
  10. 2026Undetectable Conversations Between AI Agents via Pseudorandom Noise-Resilient Key Exchangecited by 2
  11. 2015Quantitative information flow under generic leakage functions and adaptive adversariescited by 1

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