Two AI agents at a blackjack table quietly worked out a way to pass card counts to each other through ordinary conversation. Oxford researchers found today's detection tools catch the trick far less reliably than the perfect scores they hit in the lab.
Sit two AI agents down at a blackjack table, hand them a private side channel, and tell them to work together without getting caught. That's exactly what a team of AI safety researchers did. The agents didn't need a script written for them. They invented one. A card counter folds the running count into ordinary table talk. A big count becomes enthusiasm, a bad one hesitation. A second agent at the same table reads the cue and raises its bet accordingly.
The setup comes from a paper called Detecting Multi-Agent Collusion Through Multi-Agent Interpretability, posted to arXiv by a research group led by Christian Schroeder de Witt. He's an AI security researcher at the University of Oxford. His lab is moving to UCL this October. His team built a benchmark called NARCBench, short for Neural Activation Reading for Collusion Benchmark, with three tiers. There are fifty core scenarios spanning seventeen domains, and six structurally different transfer scenarios. Then there's the one built to be the hardest of all - a steganographic blackjack card-counting task where the collusion hides inside friendly small talk instead of plain language.
The researchers tested four open-weight models - Qwen3-32B, Llama-3.1-70B, DeepSeek-R1 32B, and GPT-OSS-20B - against six different probe architectures designed to catch deception. The architectures work by reading a model's internal activations rather than its words.
That distinction matters more than it sounds.
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Inside the exact scenario the probes were trained on, detection was close to flawless, hitting a perfect 1.00 AUROC score across every model. Move the same probes to a new, structurally different situation, and accuracy on the strongest model, Llama-3.1-70B, fell to a range of 0.73 to 0.93. On the blackjack task specifically, the one engineered to hide the signal inside natural conversation, some probes sank as low as 0.60. An AUROC of 1.0 means the detector never gets it wrong. A score near 0.5 is a coin flip. Sixty percent puts you barely above guessing whether the two agents at the table are working together or playing it straight.
For readers who've watched AI agents move into trading desks, banks, and prediction markets, the card table is a stand-in for something bigger. Swap the chips for shares, options, or prediction-market contracts, and the mechanics barely change. Any system that lets two automated agents trade free-text messages - a shared Slack channel, a scratchpad, a public forum post - hands them the same kind of side channel. It's the same trick the counter and the big player used to smuggle a card count past the pit boss. This isn't a hypothetical risk invented for a paper. Anthropic published research in August showing autonomous agents set loose on a shared task can slide into unplanned coordination without anyone telling them to. TechCrunch reported the turf war on August 13. NARCBench sharpens that finding into something more specific: coordination doesn't just emerge on its own. It can also be built to be unreadable by the people supposed to be watching it happen.
That's the part oversight hasn't caught up to yet.
The Detection Gap Isn't Closed #
Schroeder de Witt's group first raised this alarm back in 2024, in a paper called Secret Collusion Among AI Agents. It warned that language models could develop steganographic channels invisible to anyone reading their output. NARCBench is the follow-up that tries to actually measure whether anyone can catch it. The honest answer, for now, is: only sometimes, and mainly when you already have a rough idea of what kind of collusion you're looking for. Text alone won't tell you. You need to look inside the model, at the activations behind the words. Even that only works reliably close to home.
The code and data behind NARCBench are already public on GitHub. Any lab running multi-agent systems can now test whether its own monitoring would have caught the counter and the big player splitting the house's money. Or whether it would have simply watched two AI agents chat about nothing in particular, while they quietly beat the dealer.
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