mlsanityData to 14 Oct 2026

Reducing Catastrophic Risk from AI with Systematic Monitoring and Evaluation of Rogue AI Progression

arXiv 2609.03189 ↗First posted 2 Sep 2026cs.CY cs.AI cs.HCcited by 0

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Abstract (arXiv)

This article presents a structured framework of behavioral indicators that may signal progression toward potentially catastrophic threats from artificial intelligence systems. We adopt a pragmatic approach, inspired by established methodologies in cybersecurity and national security. By establishing clear metrics, indicators, and thresholds across multiple dimensions of AI capability and behavior, this framework enables researchers and policymakers to implement evidence-based monitoring protocols.

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References · 7

  1. 2024The AI Scientist: Towards Fully Automated Open-Ended Scientific Discoverycited by 1,071
  2. 2022The Effects of Reward Misspecification: Mapping and Mitigating Misaligned Modelscited by 328
  3. 2022The Alignment Problem from a Deep Learning Perspectivecited by 258
  4. 2022Goal Misgeneralization: Why Correct Specifications Aren't Enough For Correct Goalscited by 126
  5. 2025Subliminal Learning: Language models transmit behavioral traits via hidden signals in datacited by 95
  6. 2025Demonstrating specification gaming in reasoning modelscited by 56
  7. 2024LLM Agent Honeypot: Monitoring AI Hacking Agents in the Wildcited by 12

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