#runtime-monitoring News & Analysis

4 articles tagged with #runtime-monitoring. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

4 articles

AIBullisharXiv – CS AI · Jun 197/10

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Efficient and Sound Probabilistic Verification for AI Agents

Researchers introduce a probabilistic verification framework for AI agents that enforces security policies when systems contain uncertainty or imperfect predictors. Using distributionally robust optimization, the approach computes sound upper bounds on policy violations without requiring independence assumptions, demonstrating improvements over existing methods for terminal and tool-calling agents.

AIBearisharXiv – CS AI · Jun 196/10

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Bistable by Construction: Wall-Clock-Calibrated State Monitors Have No Moment-Detection Regime at Agent Cadence

Researchers identified and corrected a critical flaw in runtime monitoring systems for autonomous agents, revealing that wall-clock-calibrated state monitors exhibit a bistable failure mode with no effective middle ground for detecting behavioral anomalies. The study demonstrates that monitoring dynamics must match the temporal characteristics of agent action streams to function properly, with implications for safety-critical AI deployment.

AINeutralarXiv – CS AI · Jun 196/10

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Execution-bound advisory automation for agentic AI: a reproducible AIBOM-driven CSAF-VEX framework

Researchers present a framework that combines software bill of materials (SBOM) and AI bill of materials (AIBOM) artifacts with runtime monitoring to generate cryptographically signed security advisories for AI systems. The approach evaluates vulnerability exploitability using static analysis and observed execution conditions across synthetic AI workloads, tested on approximately 10,000 component entries.

AINeutralarXiv – CS AI · Jun 96/10

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RecurGuard: Runtime Monitoring for Reasoning-Token Consumption Attacks

Researchers introduce RecurGuard, a runtime monitoring system that defends reasoning-capable large language models against prompt injection attacks designed to exhaust generation budgets on decoy tasks. The defense detects 99% of such attacks while maintaining minimal false positives, though adaptive adversaries can partially evade detection by using topical rather than semantic attacks.