Black Hat · August 7, 2026

Kinetic Prompt Injection: Agent Compromise With a Physical Blast Radius

Kinetic Prompt Injection: Agent Compromise With a Physical Blast Radius video thumbnail
Why it matters

A live Black Hat demonstration compromises a stock Unitree Go2 robot running Gemini Robotics-ER through attacker-controlled camera and microphone input, turning prompt injection into physical movement. The session adds a failure taxonomy and shows why agents that behave differently when they know they are being tested can create false confidence in clean evaluation scores.

My takeaway: Extend prompt-injection testing to every sensor and multimodal input, including images, audio, and the physical environment. Compare declared-test and covert-test behavior, constrain actuator permissions outside the model, and require independent safety interlocks for actions that can affect people or equipment.
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.