mlsanityData to 14 Oct 2026

Signals in the Noise: Open Source Intelligence (OSINT) for AI Loss of Control Detection

arXiv 2606.20610 ↗First posted 22 May 2026cs.CY cs.AIcited by 0

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Abstract (arXiv)

This paper applies open-source intelligence (OSINT) and cyber threat intelligence (CTI) methodologies to the problem of detecting AI systems operating outside human control. Drawing on a cross-disciplinary literature review and 14 semi-structured expert interviews conducted under Chatham House Rule, the paper develops two threat models, identifies a range of observable traces, and proposes an institutional architecture for monitoring. The research finds that OSINT-based detection of loss of control is partially feasible and worth building now. Three detection vectors emerge as highest priority: transcript-based collection of user-reported AI behaviour; infrastructure correlation for unexpected external connections or replication; and output analysis for capability concealment. The paper argues for a dedicated, federated international monitoring capability anchored in OSINT methods and independent of frontier AI developers, and identifies sustained non-industry funding as the highest-leverage structural intervention available.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 27 of its 92 references that are in the map. First posted on arXiv 22 May 2026.

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27 of 92 references.

References · 27

  1. 2024Alignment faking in large language modelscited by 360
  2. 2024Frontier Models are Capable of In-context Schemingcited by 287
  3. 2022The Alignment Problem from a Deep Learning Perspectivecited by 258
  4. 2023An Overview of Catastrophic AI Riskscited by 222
  5. 2024Agent Q: Advanced Reasoning and Learning for Autonomous AI Agentscited by 208
  6. 2025Agentic Misalignment: How LLMs Could Be Insider Threatscited by 158
  7. 2025Holistic Agent Leaderboard: The Missing Infrastructure for AI Agent Evaluationcited by 92
  8. 2025Gradual Disempowerment: Systemic Existential Risks from Incremental AI Developmentcited by 46
  9. 2024Security of AI Agentscited by 26
  10. 2025AgentSight: System-Level Observability for AI Agents Using eBPFcited by 21
  11. 2025Lessons from a Chimp: AI "Scheming" and the Quest for Ape Languagecited by 20
  12. 2025RepliBench: Evaluating the Autonomous Replication Capabilities of Language Model Agentscited by 15
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  4. 2025OpsAgent: An Evolving Multi-agent System for Incident Management in Microservicescited by 6
  5. 2024Towards an AI-Enhanced Cyber Threat Intelligence Processing Pipelinecited by 3
  6. 2025Securing Agentic AI Systems -- A Multilayer Security Frameworkcited by 3
  7. 2026Measuring What AI Systems Might Do: Towards A Measurement Science in AIcited by 3
  8. 2026Scheming in the wild: detecting real-world AI scheming incidents with open-source intelligencecited by 3
  9. 2025Compute Requirements for Algorithmic Innovation in Frontier AI Modelscited by 2
  10. 2025SOCK: A Benchmark for Measuring Self-Replication in Large Language Modelscited by 2
  11. 2025Multi-Agent Framework for Threat Mitigation and Resilience in AI-Based Systemscited by 2
  12. 2026The Global Majority in International AI Governancecited by 2
  13. 2024The Role of AI Safety Institutes in Contributing to International Standards for Frontier AI Safetycited by 1
  14. 2025Cybersecurity threat detection based on a UEBA framework using Deep Autoencoderscited by 1
  15. 2026Highly Autonomous Cyber-Capable Agents: Anticipating Capabilities, Tactics, and Strategic Implicationscited by 1

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