External monitors outperform prompt guardrails against agent injection
Original title: Treating the Model as an Untrusted Component: Deterministic Policy Enforcement, Human Approval and Taint Tracking Against Prompt Injection in Tool-Using Agents, with Validation on Open-Weights Models
On two Qwen2.5 models, a task-bound monitor admitted 1.3% and 7.7% of attacks after models proposed the attacker's call in 64% and 84%.
The paper compares in-prompt defenses with deterministic monitors, human approval and taint tracking for tool-using LLM agents. Real evaluations used Qwen2.5-1.5B and Qwen2.5-7B, with 156 attack episodes per defence and model across 13 tasks and 12 tools. A capability-bound monitor admitted one of 33 effective attack variants, versus 18 for a naive monitor. Human-approval and taint findings come only from simulation.
Unconfirmed: Human-approval and taint results are simulation-only; real evidence covers two models and static attacks.
- A prompt guardrail moved obedience from 67% to 65% and from 86% to 55%.
- Spotlighting plus a detector reduced obedience to 12% and 0% on static attacks.
- Simulated adaptive attacks drove every guardrail stack to 100% success.
- The task-bound monitor held severe simulated actions at 0%.
- Taint-triggered human review cut residual harm of 0.69/10 by 62%.
From the original article
1 min read at arxiv.orgTool-using LLM agents act on instructions embedded in the data they read, so prompt injection is a structural rather than a prompt-wording problem.
By Mustafa S. Aljumaily · arxiv.org
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