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The article discusses methods for detecting new prompt injection patterns in AI systems after launch, including semantic search, trace-level safety scores, and tools like Braintrust, while highlighting challenges with false positives and attack taxonomy.
A second-order early warning signal for multi-turn prompt injection is introduced, based on information geometry and a statistical manifold. The method uses a meta rate derived from the second derivative of the stability parameter to predict adversarial trajectories before threshold crossing, providing proactive detection.
USAD proposes two new statistics, Variance Discrepancy and Perturbation-based Covariance Discrepancy, to capture global and local uncertainty patterns of adversarial examples, achieving superior detection performance over baseline methods.
Arc Gate is a security tool for AI agents that tracks entire conversations to detect adversarial behavioral drift across multiple turns, unlike traditional per-message checks. The author seeks teams with real agent workflows to test it.
Proposes Agent-ToM, a learning-to-monitor framework using Theory-of-Mind reasoning to detect covert malicious behavior in autonomous LLM agents by inferring beliefs and intents, outperforming baseline monitors.