The Granularity Mismatch in Agent Security: Argument-Level Provenance Solves Enforcement and Isolates the LLM Reasoning Bottleneck
Linfeng Fan, Ziwei Li, Yuan Tian, Yichen Wang, Rongsheng Li, Xiong Wang

TL;DR
This paper introduces PACT, a provenance-aware runtime monitor that enhances agent security by tracking argument origins and enforcing trust contracts, significantly improving security without sacrificing utility.
Contribution
The paper presents PACT, a novel provenance-based security mechanism for LLM agents that outperforms existing methods in security and utility across multiple models.
Findings
PACT achieves 100% security on strong models.
PACT maintains high utility, recovering up to 46.4%.
Flat invocation monitors have higher false positives/negatives.
Abstract
Tool-using LLM agents must act on untrusted webpages, emails, files, and API outputs while issuing privileged tool calls. Existing defenses often mediate trust at the granularity of an entire tool invocation, forcing a brittle choice in mixed-trust workflows: allow external content to influence a call and risk hijacked destinations or commands, or quarantine the call and block benign retrieval-then-act behavior. The key observation behind this paper is that indirect prompt injection becomes dangerous not when untrusted content appears in context, but when it determines an authority-bearing argument. We present \textsc{PACT} (\emph{Provenance-Aware Capability Contracts}), a runtime monitor that assigns semantic roles to tool arguments, tracks value provenance across replanning steps, and checks whether each argument's origin satisfies its role-specific trust contract. Under oracle…
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