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AGENT-HIJACKING

Agent-Hijacking

3 reports
All LLM Security Agentic AI Industry News Supply Chain Research Prompt Injection First Look: Security Regulatory Adversarial ML Jailbreaks Model Theft Data Poisoning AI Security Tools Security Operations
Google DeepMind Releases AI Agent Attack Taxonomy

Google DeepMind Releases AI Agent Attack Taxonomy

FIRST LOOK ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 8.7 SecurityWeek

Google DeepMind researchers have released a structured taxonomy categorising adversarial attacks against autonomous AI agents into six classes — content injection, semantic manipulation, cognitive state poisoning, behavioural control, systemic, and human-in-the-loop traps — formalising an emerging threat model for agentic AI systems. For defenders, this framework codifies attack paths that exploit the agent's inability to distinguish trusted instructions from attacker-controlled data ingested from web pages, emails, documents, and tool outputs. NIST evaluation data cited in the research shows malicious instruction injection succeeded in 57% of tested agent hijacking scenarios on average, underscoring that these are active, high-yield attack vectors rather than theoretical concerns.

Agent Hijacking: Microsoft's Defense-in-Depth Framework

Agent Hijacking: Microsoft's Defense-in-Depth Framework

ATLAS OWASP MEDIUM Moderate risk · Monitor closely ▲ 7.2 Microsoft Security Blog

Microsoft's Security Blog introduces a layered defense-in-depth model specifically designed for autonomous AI agents, which now invoke tools, modify data, and trigger workflows with minimal human oversight. The framework identifies novel threat classes — including agent hijacking, intent breaking, and supply chain compromise — that are amplified by agentic autonomy. The guidance positions application-layer architecture, permissions, and governance as the most critical controls as agent autonomy scales.

Agent Hijacking and Prompt Injection Threaten AI Payments

Agent Hijacking and Prompt Injection Threaten AI Payments

ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 7.2 Wired Security

The FIDO Alliance, backed by Google and Mastercard, is forming working groups to establish cryptographic standards for authenticating AI agent-initiated transactions, addressing risks like agent hijacking, prompt injection, and unauthorised financial actions. The initiative responds to a growing attack surface where agentic AI systems act on behalf of users without adequate authentication frameworks. Google's Agent Payments Protocol (AP2) and Mastercard's Verifiable Intent framework are being contributed as open-source foundations for the effort.

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