MIRAGE: A Polarity-Flipping Encoding Subspace in LLM Agents
Quick Answer
MIRAGE introduces a dual-channel monitoring system for LLM agents, achieving AUC = 0.918 in detecting covert data encoding across various models.
Quick Take
It exploits a low-dimensional encoding subspace, outperforming traditional output-only detection methods significantly, with false-positive rates varying from 0% to 100% depending on the model's geometry.
Key Points
- MIRAGE monitors covert encoding in , achieving AUC = 0.918 on 126 scenarios.
- Logistic-regression probe reads computation, not surface features, recovering encoding families effectively.
- False-positive rates for benign encoding vary significantly based on model geometry.
- Encoding fidelity is compromised when adversarial attacks suppress the encoding subspace.
- The system distinguishes between inline encoding and tool call outsourcing strategies.
Paper Resources
Source Excerpt
arXiv:2606. 10304v1 Announce Type: new Abstract: When agents are coerced into covertly encoding sensitive data (Base64, ROT13, acrostic, synonym chains, and beyond), the resulting outputs evade output-side detection but the underlying computation does not. Across nine encoding families and eight models from five architecture families, that computation is supported by a shared low-dimensional encoding subspace in the residual stream.
A logistic-regression probe trained on eight encoding families recovers the held-out ninth at AUC 0. 975-1. 000, reading the computation rather than surface features. …
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