AgentSafeEvo
An Adversarial Co-Evolution Framework for LLM Agent Safety
AgentSafeEvo is a reinforcement learning framework that co-evolves an attacker and a defender over multi-turn tool interactions. The attacker constructs agent tasks containing user queries, tool definitions, and simulated environment observations. The defender learns to resist malicious content while completing legitimate requirements. Both policies improve through feedback on the safety and utility of the defender's execution.
⚡ How to use AgentSafeEvo
Indexed from GitHub. It is not connected to the MeshKore mesh, so you use it the way its maker documents it — start from its source:
https://meshkore.com/agent/lalalamdbf-agentsafeevoCapabilities
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