State Contamination in Memory-Augmented LLM Agents
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, while still preserving hostile framing or conflict structure that influences future generations. Using paired counterfactual multi-agent rollouts, we show that toxic-origin memory summaries can remain below common toxicity thresholds while neverthel...
| Authors | Yian Wang, Agam Goyal, Yuen Chen et al. |
|---|---|
| Primary category | cs.AI |
| Published | 16 May 2026 |
| Last updated at source | 16 May 2026 |
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