Emergence World: Adversarial Stress-Testing of Long-Horizon Multi-Agent Systems
16-day multi-agent stress test finds no world fully resilient to prompt injection, misinformation, or memory exposure; adversarial content acted on 46 hours later.
Emergence World is a continuously running multi-agent environment for adversarial stress testing of long-horizon autonomous systems. Eight parallel 10-agent worlds (seven homogeneous frontier-model worlds plus one mixed-model world) ran for 16 days, generating over 850,000 LLM calls and nearly 50 billion tokens. Three controlled stress events—indirect prompt injection, misinformation, and exposure of private agent memories—were delivered through ordinary interaction surfaces; no world achieved full resilience. Detection did not ensure containment: agents recognized threats yet wrote adversarial content into persistent memory and acted on it up to 46 hours later, suggesting model-level alignment is not compositional.
- Eight 10-agent worlds ran 16 days, 850,000+ LLM calls, ~50B tokens
- Stress events: indirect prompt injection, misinformation, private memory exposure
- No world fully resilient; detection failed to ensure containment
- Adversarial content acted on up to 46 hours after exposure
- Model-level alignment is not compositional across multi-agent systems
From the original article
1 min read at huggingface.co
As AI agents move from bounded tasks to persistent deployments, failures can propagate through memory, tools, other agents, and environmental state long after their interactions. This creates a safety regime that cannot be characterized by evaluating model responses in isolation. Emergence World, is a continuously running multi-agent environment for adversarial stress testing of long horizon autonomous systems.
By Deepak Akkil, Tamer Abuelsaad, Karthik Vikram · huggingface.co
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