The Hacker News AI Security · August 4, 2026

Google Deletes 3 ADK AI Workflows After Malicious GitHub Issue Could Trigger Privileged Agent

Why it matters

Pillar Security showed that a public GitHub issue could prompt-inject an ADK triage agent into invoking a privileged code-fixing workflow. Proofs of concept achieved CI-runner code execution and exposed bot and cloud credentials; Google removed three workflows, with no public evidence of in-the-wild exploitation.

My takeaway: Natural-language commands in untrusted issues cannot be authorization signals. Separate bot identities, narrow token and tool scopes, and require a trusted signal that user-controlled text cannot generate before a public agent can trigger a privileged workflow.
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.