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Secret Collusion among AI Agents: Multi-Agent Deception via Steganography
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Recent capability increases in large language models (LLMs) open up applications in which groups of communicating generative AI agents solve joint tasks. This poses privacy and security challenges concerning the unauthorised sharing of information, or other unwanted forms of agent coordination. Modern steganographic techniques could render such dynamics hard to detect. In this paper, we comprehensively formalise the problem of secret collusion in systems of generative AI agents by drawing on relevant concepts from both AI and security literature. We study incentives for the use of steganography, and propose a variety of mitigation measures. Our investigations result in a model evaluation framework that systematically tests capabilities required for various forms of secret collusion. We provide extensive empirical results across a range of contemporary LLMs. While the steganographic capabilities of current models remain limited, GPT-4 displays a capability jump suggesting the need for continuous monitoring of steganographic frontier model capabilities. We conclude by laying out a comprehensive research program to mitigate future risks of collusion between generative AI models.
Forward citations
Cited by 6 Pith papers
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TraceGuard: Structured Multi-Dimensional Monitoring as a Collusion-Resistant Control Protocol
Structured five-dimension LLM monitoring plus bash heuristics yields clear attack-honest separation and ~95% safety under untrusted monitoring on BashArena, versus 0% for single-score baselines.
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NEST: Nascent Encoded Steganographic Thoughts
Frontier LLMs can embed short digit sequences in sentence acrostics (Claude Opus 4.5: 92% per-digit at D=4) but fail to jointly solve hidden reasoning tasks and encode the solution.
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Evaluating LLM Agent Collusion in Double Auctions
LLM sellers in a simulated double auction collude more when they can communicate, and urgency from an authority figure sustains collusion even when an overseer monitors them.
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SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation
A systematization-of-knowledge survey that categorizes LLM privacy risks into training data, prompts, outputs, and agents, and reviews limitations of current mitigations.
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