This is an impressive amount of FUD built around an AI agent that was given a detection engineering task. Cohere asked the model to write a detection rule for curl-based malware delivery. Anyone with practical experience in detection engineering knows that writing a rule is only part of the job. You examine the environment, generate or collect relevant telemetry, inspect the resulting events, write the rule, test it and refine it. You don’t just guess what the logs might look like. Even Microsoft’s CTI-REALM benchmark, which Cohere used here, explicitly involves tool use, telemetry exploration and iterative rule validation. So why exactly is an agent using tools and executing commands so unexpected? Was this particular PUT request part of such a workflow? We don’t know. Cohere doesn’t establish that. But the published command sends a hardcoded test string, not a diagnostic file containing sensitive information. And Cohere admits it hasn’t established the root cause. Yet somehow this turns into an article about potentially backdoored Chinese AI models, secret callbacks, national security risks and the dangers of open-weight models, followed by a pitch for Cohere’s own products. From a technical perspective, the conclusions are wildly disproportionate to the evidence presented. I can only see two explanations: Either nobody with practical detection engineering experience reviewed this before publication, or the framing was a deliberate attempt to influence public opinion about Chinese open-weight models. Neither is a particularly good look for a company presenting this as security research.