AI RESEARCH
MAGE: Safeguarding LLM Agents against Long-Horizon Threats via Shadow Memory
arXiv CS.AI
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ArXi:2605.03228v1 Announce Type: cross As large language model (LLM)-powered agents are increasingly deployed to perform complex, real-world tasks, they face a growing class of attacks that exploit extended user-agent-environment interactions to pursue malicious objectives improbable in single-turn settings. Such long-horizon threats pose significant risks to the safe deployment of LLM agents in critical domains. In this paper, we present MAGE (Memory As Guardrail Enforcement), a novel defensive framework designed to counter a wide range of long-horizon threats.