Updating the taxonomy of failure modes in agentic AI systems: What a year of red teaming taught us

In this article Why the Taxonomy Needed Updating Seven new failure modes Operational findings: What red teaming showed New mitigations What to do this quarter When the Microsoft AI Red Team published the Taxonomy of Failure Modes in Agentic AI Systems in April 2025, the goal was a shared vocabulary for a threat landscape that did not fit existing frameworks. The v1.0 taxonomy was largely forward-looking, built on practitioner interviews, cross-company threat modeling, and our own early operational experience. It identified novel failure modes unique to agentic systems (agent compromise, injection, impersonation, flow manipulation) alongside existing failure modes materially amplified in agentic contexts (memory poisoning, cross-domain prompt injection, human-in-the-loop bypass). Twelve months later, the evidence base has shifted enough to warrant a v2.0 . The update adds seven new failure mode categories, expands the mitigations section, and grounds the framework in 12 months of red team engagements against deployed agentic systems. Why the Taxonomy Needed Updating Four developments drove the revision. Open-source agentic frameworks went mainstream faster than the security community was ready for. OpenClaw, launched in January 2026, accumulated over 336,000 GitHub stars and spawned more than 2,100 agents within 48 hours of release. A security audit conducted shortly after launch identified 512 vulnerabilities including CVE-2026-25253, a one-click RCE via WebSocket hijacking. Over 1,800 exposed instances were leaking API keys and credentials within the first week, and 336 malicious plugins were found in the skills marketplace, including credential stealers masquerading as trading bots. The MCP ecosystem matured — and accumulated vulnerabilities at scale. The Model Context Protocol became the de facto standard for connecting models to external tools. In 2025, 99 CVEs were published for MCP-related software, and tool poisoning moved from theoretical risk to live attack surface. Computer-use agents moved from research to production. Agents that observe and interact with graphical interfaces introduce attack surfaces with no analogue in earlier AI security work, and expose previously human-targeted attack patterns to LLMs. The original taxonomy lacked dedicated coverage for this capability class; operational experience made clear it requires its own category. Twelve months of red team operations provided empirical grounding. The v1.0 taxonomy was forward-looking. The v2.0 update is grounded in patterns observed across real engagements with findings that confirmed some predictions, falsified others, and surfaced failure modes that were not anticipated. Seven new failure modes 1. Agentic Supply Chain Compromise. Agentic systems consume plugin registries, MCP servers, prompt templates, and third-party tool integrations, each a new supply chain ingestion point. Unlike traditional supply chain compromise, which delivers malicious code, a compromised agenti