Researchers propose double‑stratified protocol for evaluating latent monitors
Tool‑using language agents can be tricked by indirect prompt injection, a problem that has spurred research into internal monitors that flag unsafe actions before they happen. Recent studies report conflicting results, suggesting that the disagreement may stem from how evaluations are designed rather than from the monitors themselves.
Key points
- Double‑stratified protocol separates exposure from commitment in latent monitor evaluation.
- On Qwen AgentDojo, retrained probes achieve AUROC 0.853 for early unsafe tool call prediction.
- Mistral‑7B and OLMo‑2‑7B reach AUROCs 0.896 and 0.912, showing model size matters.
The authors propose a double‑stratification protocol that separates exposure to unsafe prompts from the agent’s actual commitment to unsafe behavior. On the Qwen AgentDojo grid, monitors trained with a naive mixed unsafe‑versus‑rest contrast mainly detect exposure, flagging unsafe trajectories regardless of compliance. When retrained on exposed cases only, the same probes predict the model’s unsafe tool calls up to two steps early, achieving a logistic AUROC of 0.853, matched by a TF‑IDF baseline at 0.855.
For larger models, Mistral‑7B reaches 0.896 and OLMo‑2‑7B 0.912, while naive contrasts learn different patterns. Representations show a perfect exposure score of 1.000 versus 0.908 for trained text. The study concludes that unstratified AUROC overstates monitor capability and that evaluations should stratify both exposure and commitment to obtain reliable metrics.
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