DelegateGuard: Purpose-Bound Capabilities for Delegated LLM Agent Workflows

(Context) LLM agents increasingly delegate subtasks while holding authority over sensitive data, creating confused-deputy risks: an injected delegate can exfiltrate data, amplify its authority, or act after revocation. Existing leakage benchmarks expose these failures but do not constrain the authority a delegated agent may exercise.(Methods) We present, a reference monitor that converts the initiating user's authorization into attenuable capabilities over purpose, labels, operations, sinks, and delegation depth; every data-moving action is mediated outside the LLM, and revocation cascades through the delegation graph. We formalize a trace-level safety invariant and evaluate it with an adaptive within-authority adversary and four baselines, model-action and multi-turn ecological studies, and conformance and scale checks over deterministic traces and 517, 401 Enron emails.(Results) blocks ungranted-sink exfiltration, authority amplification, revocation replay, purpose gaming, and intra-tool label violations, and dominates static allowlists on the revocation and intra-tool cases (10/10). Two open models proposed attacker-goal actions on 42/45 and 44/45 injected traces, all blocked at execution; a 90-run multi-turn study cut leakage from 0.844 to 0.20 with full task completion, at microsecond per-decision overhead. Proposition 1 characterizes exactly where attenuation-based authorization suffices, a boundary we also reproduce empirically across four observable-sink workflows.(Conclusion & Significance) Purpose-bound runtime authorization gives delegated LLM workflows a model-independent execution boundary and a precise recipe for composing it with declassification and content monitoring on externally observable sinks.