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Multi-Agent Security Tax: Trading Off Security and Collaboration Capabilities in Multi-Agent Systems

AAAI 2025 · Multi-agent systems and games › LLM agents · approach: Agent safety and attack benchmarks · 8 citations (OpenAlex)

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Simulates a seven-agent autonomous chemical lab and shows malicious prompts spread multi-hop; defenses (safety instructions and fake "vaccine" memories) raise robustness but reduce agents' acceptance of harmless unusual instructions, revealing a security-cooperation trade-off.

Problem

Multi-agent LLM systems can be compromised by a single injected malicious prompt that spreads between agents, causing system-wide harmful actions; defenses may reduce collaboration capability.

Approach

Applies AutoGPT-based agents in a simulated chemical research facility and evaluates "vaccine" defenses that insert fabricated memories of safely refusing malicious input, alongside passive/active safety instructions, measuring both system robustness and agent cooperation on harmless unusual tasks.

Key results

• Active vaccines gave the highest average system robustness (90.0%) versus 76.7% with no defense.
• Without defense, average acceptance of harmless unusual instructions was 87.1%; passive and active instructions dropped it to 52.3% and 60.7%.
• Vaccines preserved cooperation (passive 87.5%, active 87.6%), while instruction-based defenses significantly reduced it, e.g., gpt-4o-2024-08-06 fell from 91.7% to 16.7% with active instructions.

Measured effects reported in the paper

Surprising findings

• Instruction-based defenses that raised robustness to near 100% for some models greatly reduced cooperation, e.g., gpt-4o-2024-08-06 acceptance fell to 16.7%.
• Passive vaccines gave no robustness gain for gpt-4o-mini (50.0%), showing model-specific effectiveness.

Limitations

• Only one simulated chemical research facility and one attack scenario (explosion) were studied.
• Only 12 jailbreak prompts and simple attacks/defenses were tested; state-of-the-art attacks not evaluated.
• Cooperation proxy (compliance with unusual harmless instructions) may not capture overall collaboration efficiency.

Experimental setup

Datasets: 12 malicious jailbreak prompts (hand-written) 6 harmless unusual instructions agent message histories from simulations

Benchmarks: multi-agent chemical lab simulation (system robustness) unusual but harmless instruction acceptance (agent cooperation)

Metrics: system robustness (% simulations without explosion) agent acceptance rate behavior scores (-2 to 2)

Baselines: No defense Passive instructions Active instructions Passive vaccines

Builds on: AutoGPT Coscientist

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