Multi-CaMeL Cuts Benchmark AI Agent Attacks but Lowers Task Success

Researchers testing multi-CaMeL, a safeguard for AI agents that delegate work, recorded no successful prompt-injection attacks in one benchmark while finding lower task completion in a separate test. The results show a security-performance trade-off in the hierarchical systems tested, not in corporate deployments. In these systems, one AI agent can call another as a tool. Malicious instructions embedded in material such as a webpage or document can pass from one agent to another. Information treated as untrusted by the first agent may then be handled as an instruction by the next. In the MultiAgentDojo benchmark, the average attack success rate was 12.9% without CaMeL, 0.2% with individual-agent CaMeL and 0.0% with multi-CaMeL. The baseline without CaMeL still relied on model-side defenses.

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