Topic
privilege
New Research Reveals LLM Agents Often Choose Over-Privileged Tools, Posing Security Risks
A new study introduces ToolPrivBench to evaluate over-privileged tool selection in LLM agents. The research finds that agents commonly choose higher-privilege tools even when lower-privilege alternatives are sufficient, and that safety alignment does not prevent this. A privilege-aware post-training defense is proposed to reduce unnecessary high-privilege tool use.
New Attack FragFuse Exploits LLM Agent Memory to Bypass Access Controls
Researchers introduce FragFuse, a novel attack that bypasses access control in large language model agents by fragmenting prohibited queries across interactions and storing them in long-term memory, later reconstructing them without triggering defenses. The attack achieves an 86.3% average bypass success rate across multiple agent settings and exposes a critical vulnerability in memory-based AI systems.