  Recent capability increases in large language models (LLMs) open up
applications in which teams 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 the 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.
