mlsanityData to 7 Oct 2026

Codetta: High-Capacity, Keyless, and Undetectable Multi-Agent Collusion

arXiv 2609.28900 ↗First posted 24 Sep 2026cs.CR cs.MAcited by 0

On the map: LLM agents and tool use → Agent threats, guardrails and governance

Abstract (arXiv)

Multi-agent systems built on large language models (LLMs) are increasingly deployed in high-stakes settings such as finance, healthcare, and software engineering, where agents coordinate through natural-language messages. The same channels, however, let colluding agents exfiltrate confidential information or coordinate unauthorized actions, and steganography can hide such communication inside outputs that look ordinary to an auditor reading the transcript. Existing provably undetectable LLM steganography protocols are not suited to realistic deployments. High-capacity schemes assume a symmetric setting where the receiver can reproduce the sender's output distribution, the state-of-the-art protocol for asymmetric agents has very low capacity, and most approaches rely on a pre-shared secret key. We make the threat of undetectable agent collusion concrete with Codetta, a high-capacity steganographic protocol for independently deployed agents in realistic asymmetric settings. Codetta combines a shared public model that estimates the communication channel, a sampling mechanism that preserves the sender's output distribution, and an adaptive error-correcting code. It further removes the pre-shared key through a steganographic key exchange that lets independently deployed agents establish a shared key while keeping the transcript computationally indistinguishable from ordinary model outputs. Across three agent workloads and three sender models, Codetta achieves up to 94× the capacity of the state-of-the-art asymmetric protocol, and its key exchange establishes a shared key with about 80k visible tokens at an empirically certified failure probability of at most 4.1× 10-3. These results show that effectively undetectable collusion is becoming feasible between independently deployed agents, so auditing must go beyond inspecting communication transcripts.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 35 of its 60 references that are in the map. First posted on arXiv 24 Sep 2026.

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35 of 60 references.

Closest ancestors · 4

Same direction, newest first

  1. 2026Undetectable Conversations Between AI Agents via Pseudorandom Noise-Resilient Key Exchange6 Apr
  2. 2025LlamaFirewall: An open source guardrail system for building secure AI agents6 May
  3. 2025Firewalls to Secure Dynamic LLM Agentic Networks3 Feb
  4. 2024Secret Collusion among AI Agents: Multi-Agent Deception via Steganography12 Feb

Other references · 31

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  7. 2024Large Language Model based Multi-Agents: A Survey of Progress and Challengescited by 752
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  9. 2023MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoningcited by 311
  10. 2023Robust Distortion-free Watermarks for Language Modelscited by 279
  11. 2023InterCode: Standardizing and Benchmarking Interactive Coding with Execution Feedbackcited by 229
  12. 2023Undetectable Watermarks for Language Modelscited by 208
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  3. 2022Sionna: An Open-Source Library for Next-Generation Physical Layer Researchcited by 143
  4. 2019Neural Linguistic Steganographycited by 73
  5. 2023Preventing Language Models From Hiding Their Reasoningcited by 62
  6. 2024Hidden in Plain Text: Emergence & Mitigation of Steganographic Collusion in LLMscited by 52
  7. 2024zkLLM: Zero Knowledge Proofs for Large Language Modelscited by 51
  8. 2024No Free Lunch in LLM Watermarking: Trade-offs in Watermarking Design Choicescited by 47
  9. 2024Pseudorandom Error-Correcting Codescited by 40
  10. 2024Can Large Language Models Grasp Legal Theories? Enhance Legal Reasoning with Insights from Multi-Agent Collaborationcited by 14
  11. 2025SparSamp: Efficient Provably Secure Steganography Based on Sparse Samplingcited by 12
  12. 2024Excuse me, sir? Your language model is leaking (information)cited by 11
  13. 2025When Autonomy Goes Rogue: Preparing for Risks of Multi-Agent Collusion in Social Systemscited by 9
  14. 2026A Decision-Theoretic Formalisation of Steganography With Applications to LLM Monitoringcited by 6
  15. 2024Provably Robust and Secure Steganography in Asymmetric Resource Scenariocited by 5
  16. 2026Efficient Provably Secure Linguistic Steganography via Range Codingcited by 3
  17. 2025Provable Secure Steganography Based on Adaptive Dynamic Samplingcited by 2
  18. 2026Conceptual Steganographycited by 1
  19. 2026Steganography Without Modification: Hidden Communication via LLM Seedscited by 1

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