A sketch of an AI control safety case

2025-01-28

AI Summary

To address high-risk failures in monitoring LLM-based agents—such as unauthorized sensitive data leakage—this paper introduces the *Control Safety Case* framework, shifting AI safety assurance from capability alignment to rigorous verification of control robustness. Methodologically, it integrates red-teaming adversarial testing, formal modeling of control effectiveness, agent behavior stimulation, and conservative probabilistic extrapolation to construct a testable, three-layer evidential chain. This work provides the first systematic definition of the framework and delivers a structured, reusable safety argument template for pre-deployment trustworthiness assessment of high-risk LLM agents. The contribution advances AI governance by supplying auditable, reproducible technical evidence, significantly enhancing the quantifiability and rigor of failure-mode analysis for safety-critical controls. (124 words)

Technology Category

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

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

As LLM agents gain a greater capacity to cause harm, AI developers might increasingly rely on control measures such as monitoring to justify that they are safe. We sketch how developers could construct a"control safety case", which is a structured argument that models are incapable of subverting control measures in order to cause unacceptable outcomes. As a case study, we sketch an argument that a hypothetical LLM agent deployed internally at an AI company won't exfiltrate sensitive information. The sketch relies on evidence from a"control evaluation,"' where a red team deliberately designs models to exfiltrate data in a proxy for the deployment environment. The safety case then hinges on several claims: (1) the red team adequately elicits model capabilities to exfiltrate data, (2) control measures remain at least as effective in deployment, and (3) developers conservatively extrapolate model performance to predict the probability of data exfiltration in deployment. This safety case sketch is a step toward more concrete arguments that can be used to show that a dangerously capable LLM agent is safe to deploy.

Problem

• Ensuring AI safety in deployment
• Preventing data exfiltration by AI models
• Validating effectiveness of control measures

Innovation

• Control safety case
• Red team evaluation
• Conservative performance extrapolation