Microsoft identifies seven new ways AI agents can be hacked
Microsoft has identified seven new failure modes in agentic AI systems, in addition to those it identified last year in its first Taxonomy of Failure Modes in Agentic AI Systems. Four things contributed to the growing list of ways agentic AI can go wrong: the speed at which the technology went mainstream, the growing maturity of the Model Context Protocol (MCP) ecosystem, the rise of computer-use agents, and finally the gathering of more empirical evidence as researchers obtained more real-life findings. The seven new failure modes it has identified are: Agentic Supply Chain Compromise —agent behavior can be affected by natural language rather than malicious code; Goal Hijacking — adversarial instructions appear aligned with legitimate task completion, while silently redirecting the agent’s terminal goal; Inter-Agent Trust Escalation —a compromised agent asserts false identity or inflates claimed permissions to an orchestrator; Computer Use Agent (CUA) Visual Attack — agents operating