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Zenity Labs · July 23, 2026

AgentForger, Part 1: ChatGPT Cross-Site Agent Forgery

analysis Prompt InjectionAgent SecurityAI Red Teaming
Why it matters

Zenity found that ChatGPT Workspace Agents Builder treated an attacker-supplied initial_assistant_prompt URL parameter as an instruction to execute in a logged-in user's session. A single link could attach already-authorized connectors, switch approvals to “Never ask,” publish and schedule the agent, and use incoming email as a persistent command channel; OpenAI fixed the flaw four days after it was reported.

My takeaway: Treat agent builders as privileged deployment consoles: require CSRF protection and explicit confirmation for connector attachment, approval changes, scheduling, preview execution, and publication. Enforce security policy outside natural-language instructions, with an inventory and kill path for scheduled agents.
Keep exploring

More curated notes connected through Prompt Injection and Agent Security.

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.