REALM: A least-privilege method for identifying assets required for agent tasks using dependency graphs

Researchers from the Institute for Network Sciences and Cyberspace at Tsinghua University and Kyungpook National University published the paper "REALM: Identifying Minimal Task Assets for LLM Agents" (arXiv:2610.39155) in October 2026, proposing a least-privilege method that first identifies the assets required for an agent task and then confines them, together with permitted usage, in a container. The method first statically classifies tool parameters into four categories, uses a dependency graph to carry assets whose identity is unknown, fixes the upper bound of the candidate pool by task semantics before reading evidence, requires subset and scope checks for admission, and then implements it with Asset Files in the container and a Partial API with fixed resource parameters. On AgentDyn, among 560 baseline injection cases, 93 attacks succeeded (16.6%); after adding REALM, 1 remained (0.2%), but the clean task completion rate dropped from 78.3% to 40.0%, and under injection conditions from 71.6% to 36.4%.