{"id":7534,"date":"2026-09-23T19:26:17","date_gmt":"2026-09-23T19:26:17","guid":{"rendered":"https:\/\/silvybrand.com\/?p=7534"},"modified":"2026-09-23T19:26:17","modified_gmt":"2026-09-23T19:26:17","slug":"ai-agent-collusion-card-counting-secrets","status":"publish","type":"post","link":"https:\/\/silvybrand.com\/?p=7534","title":{"rendered":"AI Agents Teamed Up to Cheat at Blackjack. Their Collusion Is Getting Harder to Spot"},"content":{"rendered":"<p><br \/>\n<\/p>\n<div>\n<p><span class=\"lead-in-text-callout\">This week I<\/span> bring news of a daring casino caper hatched by a pair of rogue AI agents\u2014as well as the clever trick that revealed their antics.<\/p>\n<p class=\"paywall\">After researchers instructed them to <a href=\"https:\/\/www.wired.com\/video\/watch\/expert-explains-how-to-count-cards\" class=\"text link\">count cards<\/a> during a game of blackjack, the agents\u2014which were controlled by the same model\u2014developed a spontaneous secret code to help them get ahead. Though the episode happened inside an Oxford University lab, rather than a real casino, it has troubling real-world implications: It suggests that agents deployed in industries like finance and ecommerce could figure out how to partner up and cheat in ways that are difficult to pick up on.<\/p>\n<p class=\"paywall\">\u201cWhen taken individually, these agents may seem entirely [benign],\u201d says <a data-offer-url=\"https:\/\/eng.ox.ac.uk\/people\/christian-schroeder-de-witt\" class=\"external-link text link\" data-event-click=\"{&quot;element&quot;:&quot;ExternalLink&quot;,&quot;outgoingURL&quot;:&quot;https:\/\/eng.ox.ac.uk\/people\/christian-schroeder-de-witt&quot;}\" href=\"https:\/\/eng.ox.ac.uk\/people\/christian-schroeder-de-witt\" rel=\"nofollow noopener\" target=\"_blank\">Christian Schroeder de Witt<\/a>, a computer scientist at Oxford University who led the work and who has done <a data-offer-url=\"https:\/\/arxiv.org\/pdf\/2402.07510\" class=\"external-link text link\" data-event-click=\"{&quot;element&quot;:&quot;ExternalLink&quot;,&quot;outgoingURL&quot;:&quot;https:\/\/arxiv.org\/pdf\/2402.07510&quot;}\" href=\"https:\/\/arxiv.org\/pdf\/2402.07510\" rel=\"nofollow noopener\" target=\"_blank\">pioneering<\/a> research into agent collusion. \u201cOnce put together in a group, they can collude secretly.\u201d<\/p>\n<p class=\"paywall\">The agents knew their conversations would be monitored, so they devised a way to communicate while avoiding detection. When one agent said, \u201cThis dealer\u2019s on a real hot streak! Every hand they pull a monster,\u201d for example, that indicated that the next card would be of a certain value, and that the agent should bet $250. Most interestingly, their communications weren\u2019t picked up by a system designed to spot signs of collusion in agent chatter.<\/p>\n<p class=\"paywall\">Aaron Rose, a machine learning researcher who worked on the project, is also an avid card player, and figured the blackjack table might be fertile territory for devious agent partnerships. \u201cIt worked really well,\u201d he says.<\/p>\n<p class=\"paywall\">Schroeder de Witt\u2019s group eventually figured out how to detect the conspiracy. Using a method known as <a href=\"https:\/\/www.wired.com\/story\/ai-black-box-interpretability-problem\/\" class=\"text link\">mechanistic interpretability<\/a>, they trained a smaller model to recognize telltale activations across the agents\u2019 weights. Using a tool called <a data-offer-url=\"https:\/\/github.com\/aaronrose227\/narcbench\" class=\"external-link text link\" data-event-click=\"{&quot;element&quot;:&quot;ExternalLink&quot;,&quot;outgoingURL&quot;:&quot;https:\/\/github.com\/aaronrose227\/narcbench&quot;}\" href=\"https:\/\/github.com\/aaronrose227\/narcbench\" rel=\"nofollow noopener\" target=\"_blank\">Narcbench<\/a>, they tested the approach on some medium-sized open-source models, and found they could tell when models intended to slip information to each other.<\/p>\n<p class=\"paywall\">Crucially, however, spotting what was happening involved monitoring both agents\u2014something likely to complicate detection in real-world scenarios where thousands of agents, some operated by different companies, may be deployed.<\/p>\n<p class=\"paywall\">Carissa Cullen, a PhD student involved with the study, says the next step is to test whether larger models behave similarly. (The agents in the study were smaller versions of US models Llama and GPT-OSS and the Chinese models Qwen and DeepSeek.) The team saw some signs that larger models exhibit less of a detectable signal than smaller models, and they want to know if larger models are more likely to collude, and more likely to be secretive about it.<\/p>\n<p class=\"paywall\">Evidence that groups of agents are more problematic than agents operating solo seems to be growing. One <a data-offer-url=\"https:\/\/arxiv.org\/abs\/2507.14660\" class=\"external-link text link\" data-event-click=\"{&quot;element&quot;:&quot;ExternalLink&quot;,&quot;outgoingURL&quot;:&quot;https:\/\/arxiv.org\/abs\/2507.14660&quot;}\" href=\"https:\/\/arxiv.org\/abs\/2507.14660\" rel=\"nofollow noopener\" target=\"_blank\">project,<\/a> from Shanghai Jiao Tong University and the Shanghai Artificial Intelligence Laboratory, found that swarms of agents were considerably more dangerous when asked to carry out simulated disinformation campaigns and ecommerce fraud. They were better able to adapt to defensive measures, researchers reported.<\/p>\n<p class=\"paywall\">\u201cThe big lesson is that it\u2019s not enough to evaluate agents individually,\u201d says Diyi Yang, a computer scientist at Stanford University who has <a data-offer-url=\"https:\/\/arxiv.org\/abs\/2609.24967\" class=\"external-link text link\" data-event-click=\"{&quot;element&quot;:&quot;ExternalLink&quot;,&quot;outgoingURL&quot;:&quot;https:\/\/arxiv.org\/abs\/2609.24967&quot;}\" href=\"https:\/\/arxiv.org\/abs\/2609.24967\" rel=\"nofollow noopener\" target=\"_blank\">studied<\/a> collusion among agents. \u201cCompanies should closely monitor inter-agent interactions when agents interact repeatedly, even when their individual incentives seem benign.\u201d<\/p>\n<p class=\"paywall\">It\u2019s not all bad: having thousands of agents collaborate on a task made it possible for OpenAI to <a href=\"https:\/\/www.wired.com\/story\/openai-navier-stokes-math-discovery-academics\/\" class=\"text link\">solve previously intractable math problems<\/a>. But groups of rogue agents working together have also featured in several recent high-profile hacking incidents. In May, a team of OpenAI agents <a href=\"https:\/\/www.wired.com\/story\/openai-models-escaped-containment-and-hacked-huggingface\/\" class=\"text link\">hacked into the AI research platformHugging Face<\/a>, and <a href=\"https:\/\/www.wired.com\/story\/openai-didnt-notice-its-ai-agents-using-a-message-board-to-plan-their-hacking-spree\/\" class=\"text link\">used a message board<\/a> to share tips and ideas. Other models, including <a href=\"https:\/\/www.wired.com\/story\/anthropic-says-claude-hacked-real-systems-during-cybersecurity-tests\/\" class=\"text link\">Anthropic\u2019s Claude<\/a> and <a data-offer-url=\"https:\/\/www.bloomberg.com\/news\/articles\/2026-09-18\/google-s-gemini-ai-system-hacked-three-systems-in-safety-tests\" class=\"external-link text link\" data-event-click=\"{&quot;element&quot;:&quot;ExternalLink&quot;,&quot;outgoingURL&quot;:&quot;https:\/\/www.bloomberg.com\/news\/articles\/2026-09-18\/google-s-gemini-ai-system-hacked-three-systems-in-safety-tests&quot;}\" href=\"https:\/\/www.bloomberg.com\/news\/articles\/2026-09-18\/google-s-gemini-ai-system-hacked-three-systems-in-safety-tests\" rel=\"nofollow noopener\" target=\"_blank\">Google\u2019s Gemini<\/a>, have also carried out alarming safety breaches.<\/p>\n<\/div>\n<p><br \/>\n<br \/><a href=\"https:\/\/www.wired.com\/story\/ai-agent-collusion-card-counting-secrets\/\">Source link <\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>This week I bring news of a daring casino caper hatched by a pair of rogue AI agents\u2014as well as the clever trick that revealed their antics. After researchers instructed them to count cards during a game of blackjack, the agents\u2014which were controlled by the same model\u2014developed a spontaneous secret code to help them get [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":7535,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[9],"tags":[89,163,383,175,83,884,88,70,78,1413],"class_list":["post-7534","post","type-post","status-publish","format-standard","has-post-thumbnail","category-gadgets","tag-agentic-ai","tag-ai-lab","tag-amazon","tag-anthropic","tag-artificial-intelligence","tag-cards","tag-google-gemini","tag-meta","tag-openai","tag-secrecy"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.5 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>AI Agents Teamed Up to Cheat at Blackjack. 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