3 articles tagged with #runtime-security. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers have identified a new security vulnerability called 'causality laundering' in AI tool-calling systems, where attackers can extract private information by learning from system denials and using that knowledge in subsequent tool calls. They developed the Agentic Reference Monitor (ARM) system to detect and prevent these attacks through enhanced provenance tracking.
Researchers propose a five-plane reference architecture for governing production AI agents in enterprise environments, addressing security gaps where traditional data-boundary controls fail. The system uses composite principals, capability attenuation, and structured audit trails to manage delegated agent actions that could otherwise transform business processes without proper authorization.
Researchers propose a trust framework for AI agent skills—reusable code packages that extend language models—treating them as untrusted by default until verified. The approach introduces verification levels, capability gates, and correctness criteria to enable sustainable human-in-the-loop oversight without operational bottlenecks.
