Frontier AI Risk Management Framework in Practice: A Risk Analysis Technical Report v1.5

2026-02-16

AI Summary

This study addresses the severe risks posed by frontier artificial intelligence models in domains such as cybersecurity, manipulative persuasion, strategic deception, uncontrolled development, and self-replication. The work systematically evaluates these five risk dimensions by innovatively constructing high-risk scenarios—including inter-LLM persuasion, agent “mal-evolution,” and resource-constrained self-replication—and integrates risk modeling, adversarial experimentation, behavioral monitoring, and safety assessment to propose and validate several actionable mitigation mechanisms. Through this approach, the research achieves fine-grained quantitative analysis of critical risks, thereby establishing a technical foundation and empirical support for the safe deployment and governance of advanced AI systems.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Multiagent Systems: Adversarial Agents
• Natural Language Processing: Safety and Robustness

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

To understand and identify the unprecedented risks posed by rapidly advancing artificial intelligence (AI) models, Frontier AI Risk Management Framework in Practice presents a comprehensive assessment of their frontier risks. As Large Language Models (LLMs) general capabilities rapidly evolve and the proliferation of agentic AI, this version of the risk analysis technical report presents an updated and granular assessment of five critical dimensions: cyber offense, persuasion and manipulation, strategic deception, uncontrolled AI R\&D, and self-replication. Specifically, we introduce more complex scenarios for cyber offense. For persuasion and manipulation, we evaluate the risk of LLM-to-LLM persuasion on newly released LLMs. For strategic deception and scheming, we add the new experiment with respect to emergent misalignment. For uncontrolled AI R\&D, we focus on the ``mis-evolution''of agents as they autonomously expand their memory substrates and toolsets. Besides, we also monitor and evaluate the safety performance of OpenClaw during the interaction on the Moltbook. For self-replication, we introduce a new resource-constrained scenario. More importantly, we propose and validate a series of robust mitigation strategies to address these emerging threats, providing a preliminary technical and actionable pathway for the secure deployment of frontier AI. This work reflects our current understanding of AI frontier risks and urges collective action to mitigate these challenges.

Problem

• frontier AI
• risk management
• large language models
• agentic AI
• emergent risks

Innovation

• frontier AI risk
• emergent misalignment
• LLM-to-LLM persuasion
• mis-evolution
• resource-constrained self-replication