Safeguarding LLM Agents against Long-Horizon Threats via Shadow Memory
Quick summary
arXiv:2605.03228v2 Announce Type: replace-cross Abstract: 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 ShadowMem, a novel defensive framework designed to counter a wide range of long-horizon threats. Inspired by the "shadow stack" abstractio
Key takeaways
- arXiv:2605.03228v2 Announce Type: replace-cross Abstract: 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 ShadowMem, a novel defensive framework designed to counter a wide range of long-horizon threats.
Why it matters
The value of this work lies as much in how it was tested as in the claim itself. Sample design, baselines, uncertainty and replication help separate a laboratory result from real-world impact.
Member comments