Safeguarding LLM Agents against Long-Horizon Threats via Shadow Memory
Factual evidence
What the source reports
Researchers introduced ShadowMem, a defensive architecture designed to protect LLM agents against long-horizon, multi-turn security attacks.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.CL — Computation and Language · RESEARCH
- Published by source
- 24 September 2026
- Collected by OneBench
- 25 Sept 2026, 03:01 UK
Stored source excerpt
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…
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The factual summary is a OneBench synthesis, not a quotation or independent verification. Collection time is not publication time. Open the source for its full context; related reporting can share the same underlying announcement.
OneBench interpretation
Institutional assessment
So what
Multi-turn interaction vulnerabilities create new security threat vectors as financial institutions deploy autonomous agents for complex workflows.
Do what
Review agent architecture safety controls with the team responsible for enterprise security tooling.