CAMLIS · November 14, 2025

Attack Surfaces in Computer Use Agents: A Practical Taxonomy

Attack Surfaces in Computer Use Agents: A Practical Taxonomy video thumbnail
Why it matters

Microsoft's AI Red Team maps seven persistent computer-use-agent risks across UI deception, remote code execution, reasoning leakage, human-approval bypass, indirect prompt injection, identity ambiguity, and emergent content harms. Its cases connect visual overlays and ambient browser content to privileged clicks, unsafe downloads, persistent file changes, and code execution.

My takeaway: Test the entire perception-reasoning-action loop with dynamic overlays, hostile pages, downloaded files, and changing DOM state. Distinguish agent actions from user actions, re-authenticate sensitive flows, block writes to privileged paths, disable agent-driven PWA installation, and isolate downloads before execution.
Keep exploring

More curated notes connected through Agent Security and Prompt Injection.

OpenAI News · framework

OpenAI’s Frontier Governance Framework

OpenAI's 22-page Frontier Governance Framework maps its frontier-model processes to California's Transparency in Frontier AI Act and the EU AI Act's general-purpose AI code. It documents lifecycle risk assessment, cyber-offense and other risk tiers, mitigation and residual-risk decisions, critical-incident handling, security risk management, model reporting, external review, responsibility allocation, and change control.

OWASP GenAI Security Project · guide

OWASP Top 10 for Agentic Applications for 2026

OWASP's community guide organizes agentic-system risk into ten categories, including goal hijacking, tool misuse, identity and privilege abuse, memory poisoning, insecure inter-agent communication, cascading failures, and rogue-agent behavior. It provides a shared taxonomy and mitigation starting point rather than a certification checklist or evidence that a deployed system is secure.

OECD.AI Wonk · guide

A five-step roadmap to closing the AI evaluation gap

The roadmap addresses evaluation results that overstate real-world performance or fail to transfer across deployment contexts. Its five steps balance standardized and local tests, evaluate throughout the lifecycle, build qualified assurance and communication capacity, tailor tests to each value-chain actor and technology, and use a coordinated, trusted process for updating methods.