The Model Knows Better: Defending Medical Multi-Agent Systems Against Prompt Injection

The paper investigates prompt injection attacks on medical multi‑agent systems that use large language models (LLMs) for clinical decision support. Across two systems, two model families, and three benchmarks, a single injected passage can cause incorrect decisions in up to 61% of cases, and existing defenses only partially mitigate the threat. The authors propose a training‑free defense that verifies each clinical judgment against the patient’s findings, which reduces attack success on MedQA to 4‑7% while preserving accuracy on non‑attacked cases.

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• llms
• agents
• benchmarks