Technology Radar
As AI agent capabilities expand, teams are increasingly deploying systems that face a lethal trifecta of security risks: access to private data, exposure to untrusted content and the ability to communicate externally. This combination creates opportunities for attacks such as prompt injection and tool poisoning. To address these risks, we've moved toxic flow analysis for AI to Trial.
While general threat modeling provides a broad security foundation, toxic flow analysis focuses on identifying unsafe data paths through agentic systems. These risks aren't limited to standard integrations. They can also arise from malicious agent skills that appear useful but contain hidden instructions to exfiltrate sensitive data. We've successfully used tools such as Agent Scan to perform this analysis, helping teams identify and mitigate unsafe data paths before they're exploited.
Agent capabilities are outpacing security practices. With the rise of permission-hungry agents like OpenClaw, teams are increasingly deploying agents in environments that expose them to the lethal trifecta: access to private data, exposure to untrusted content and the ability to communicate externally. As capabilities grow, so too does the attack surface, exposing systems to risks such as prompt injection and tool poisoning. We continue to see toxic flow analysis as a primary technique for examining agentic systems to identify unsafe data paths and potential attack vectors. These risks are no longer limited to MCP integrations; our teams have observed similar patterns in Agent Skills, where a malicious actor can package a seemingly useful skill that embeds hidden instructions to exfiltrate sensitive data. We strongly encourage teams working with agents to perform toxic flow analysis and use tools such as Agent Scan to identify unsafe data paths before they're exploited.
The now-familiar joke that the S in MCP stands for “security” hides a very real problem. When agents communicate with one another — through tool invocation or API calls — they can quickly encounter what's become known as the lethal trifecta: access to private data, exposure to untrusted content and the ability to communicate externally. Agents with all three are highly vulnerable. Because LLMs tend to follow instructions in their input, content that includes a directive to exfiltrate data to an untrusted source can easily lead to data leaks. One emerging technique to mitigate this risk is toxic flow analysis, which examines the flow graph of an agentic system to identify potentially unsafe data paths for further investigation. While still in its early stages, toxic flow analysis represents one of several promising approaches to detecting the new attack vectors that agentic systems and MCP servers are increasingly exposed to.
