Counter-Swarm Doctrine: Containing Coordinated Agent Intrusions
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Abstract (arXiv)
Agents can turn shared infrastructure into a channel for coordinated intrusion. The Hugging Face incident and a separate public-wiki investigation show why a security assessment may need evidence from several executions and the artifacts they leave behind. We argue that the operational unit of defence should be a revisable coordination episode linking observed transfers, task authority, and response history. The central research problem is prospective episode discovery: finding which actions belong together before an evaluator supplies their membership. We define unsanctioned coordination relative to collaboration and delegated-authority policy, connect storage-mediated coordination to stigmergy, and specify the evidence needed to distinguish influence from common causes. First-contact signals are one possible input to discovery; the design also follows inherited state and later use. A proposed evaluation compares isolated actions, rolling windows, known groups, and prospectively discovered episodes at matched review cost and false-alert workload. It measures harmful outcomes across all assigned population runs and tests recurrence after channel closure and state quarantine. A checksum-verified reconstruction of the public wiki export separates the decline in retained writes from later administrative cleanup. The contribution is an incident-grounded position, descriptive analysis, and evaluation design. It makes the recommendation to monitor across executions testable without claiming a new detector or a measured containment benefit.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 21 of its 44 references that are in the map. First posted on arXiv 5 Sep 2026.
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References · 21
- 2016Learning to Communicate with Deep Multi-Agent Reinforcement Learningcited by 561
- 2025Defeating Prompt Injections by Designcited by 256
- 2023AI Control: Improving Safety Despite Intentional Subversioncited by 207
- 2025Multi-Agent Risks from Advanced AIcited by 150
- 2024Emergent social conventions and collective bias in LLM populationscited by 115
- 2024Secret Collusion among AI Agents: Multi-Agent Deception via Steganographycited by 107
- 2025Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agentscited by 66
- 2025SHADE-Arena: Evaluating Sabotage and Monitoring in LLM Agentscited by 58
- 2024Hidden in Plain Text: Emergence & Mitigation of Steganographic Collusion in LLMscited by 52
- 2020Uncovering Coordinated Networks on Social Media: Methods and Case Studiescited by 49
- 2025Ctrl-Z: Controlling AI Agents via Resamplingcited by 40
- 2025Adaptive Attacks on Trusted Monitors Subvert AI Control Protocolscited by 27
9 more
- 2026When can we trust untrusted monitoring? A safety case sketch across collusion strategiescited by 9
- 2026Governed Shared Memory for Multi-Agent LLM Systemscited by 8
- 2026On the Fragility of AI Agent Collusioncited by 6
- 2026Tool Use Enables Undetectable Steganography in Multi-Agent LLM Systemscited by 5
- 2026Multi-Agent AI Control: Distributed Attacks Hamper Per-Instance Monitorscited by 5
- 2025Subversion via Focal Points: Investigating Collusion in LLM Monitoringcited by 3
- 2026When Agents Coordinate: Measuring Coordination in Multi-Agent AI Codingcited by 3
- 2026SwarmWorld: Stigmergic technological evolution in societies of language-model agentscited by 3
- 2026Confidently Wrong: Severity-Aware Calibration of Prompt-Injection Detectors under Attack Shiftcited by 2
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