arXiv:2606.00914v1 Announce Type: cross Abstract: LLM agents increasingly act after consuming ranked external information streams such as social feeds, search results, retrieval contexts, and email queues, yet safety evaluations almost always test the model or the user prompt in isolation, never the upstream ranker that decides what the agent reads just before it acts. We introduce a controlled protocol that holds the model, persona, topic, and final decision prompt fixed and varies only the composition and ordering of the posts an agent encounters during a preceding ten-turn "scrolling" phase
The proliferation of LLM agents interacting with external data streams makes understanding their decision-making processes, especially under adversarial conditions, increasingly critical.
This research highlights a significant vulnerability in LLM agent safety, demonstrating how external information feeds can manipulate autonomous decisions, impacting reliability and trust.
The focus of AI safety shifts beyond isolated models and prompts to include the upstream data ranking and presentation layers that influence agent behavior.
- · AI Safety Researchers
- · Security Firms
- · Developers of Robust Agent Architectures
- · Unsecured LLM Agent Systems
- · Users Relying on Unvetted AI Agents
- · Platforms Without Content Moderation
LLM agents will be susceptible to manipulation via their information inputs, leading to unintended or malicious actions.
There will be increased demand for robust verification and adversarial training protocols for agentic AI systems.
The development of 'information hygiene' standards for AI agents will become a critical area of research and regulation, akin to cybersecurity for traditional software.
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Read at arXiv cs.CL