Cisco has open sourced a behavior detection engine that watches AI agents, to help security teams prevent rogue agents from going off the rails like they did with the Hugging Face hack – or worse. It’s called Project Quarry , and it aims to provide a “shared language” for evaluating agent behavior, along the lines of what Snort , which Cisco also maintains, did for detecting malicious network traffic and what YARA did for rule-based malware identification. Most detection tools rely on specific interactions with an AI model or agent. Did it attempt prompt injection? Or did the agent call an attack tool? “But what is missing is the entire context under which something may occur,” Amy Chang, head of AI threat and security research at Cisco, said in an exclusive interview with The Register . She likened this to credit card fraud detection. Without context, individual transactions such as a small test charge or a single purchase in a location far away from a card-holder’s usual location may not raise any flags. “And then you try again,” making another fraudulent purchase, “and that went through, too. So now I’m gonna buy the PS5,” Chang said. “Those are the things that Quarry addresses by taking into consideration the entire agent session,” she explained. “One tool call may seem in-scope until you realize that that next step is taking that information and sending it to an attacker-controlled inbox or server.” Even data theft is a sequence and not a single agentic action. Agentic AI security firm Invariant Labs demonstrated this with a proof-of-concept attack against GitHub’s MCP server that hijacked a user’s agent via a malicious GitHub Issue, and coerced it into leaking data from private repositories. Simply reading a configuration file, or sending a request to an external endpoint may be legitimate agentic actions. However, combining the two can lead to data exfiltration – and shows “why action relationships matter,” wrote Chang and fellow Cisco researchers Adam Swanda, Ravikumar Balakrishnan, and Konstantin Berlin in a blog post about Quarry shared with The Register ahead of publication
“A policy can produce a finding when the trace shows a sensitive file read followed within a defined window by an external send,” they wrote. “The risk comes from the relationship between the two actions, so the finding should preserve evidence from both."…