State Contamination in Memory-Augmented LLM Agents
📰 ArXiv cs.AI
Learn how memory-augmented LLM agents can be vulnerable to state contamination, compromising safety and reliability
Action Steps
- Identify potential sources of state contamination in memory-augmented LLM agents
- Analyze the effects of memory laundering on model outputs and safety
- Implement detectors to identify toxic or adversarial context in memory summaries
- Develop strategies to mitigate state contamination and ensure reliable model performance
- Evaluate the effectiveness of these strategies in preventing memory laundering
Who Needs to Know This
AI researchers and developers working with LLM agents and memory-augmented architectures will benefit from understanding this concept to ensure the safety and reliability of their models
Key Insight
💡 Memory laundering can compress toxic or adversarial context into memory summaries, evading standard detectors and compromising model safety
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🚨 State contamination in memory-augmented LLM agents can compromise safety and reliability! 🤖
Key Takeaways
Learn how memory-augmented LLM agents can be vulnerable to state contamination, compromising safety and reliability
Full Article
Title: State Contamination in Memory-Augmented LLM Agents
Abstract:
arXiv:2605.16746v1 Announce Type: new Abstract: LLM agents increasingly rely on persistent state, including transcripts, summaries, retrieved context, and memory buffers, to support long-horizon interaction. This makes safety depend not only on individual model outputs, but also on what an agent stores and later reuses. We study a failure mode we call memory laundering: toxic or adversarial context can be compressed into memory summaries that no longer appear toxic under standard detectors, whil
Abstract:
arXiv:2605.16746v1 Announce Type: new Abstract: LLM agents increasingly rely on persistent state, including transcripts, summaries, retrieved context, and memory buffers, to support long-horizon interaction. This makes safety depend not only on individual model outputs, but also on what an agent stores and later reuses. We study a failure mode we call memory laundering: toxic or adversarial context can be compressed into memory summaries that no longer appear toxic under standard detectors, whil
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