前沿AI的安全案例模板：一种网络无能力论证
Safety case template for frontier AI: A cyber inability argument
AI总结：
本文提出一种针对前沿AI攻击性网络能力的安全案例模板，利用CAE框架将风险模型、代理任务和能力评估整合为连贯明确的论证，以证明模型不具备不可接受的网络风险能力。
摘要：
前沿人工智能（AI）系统对社会构成日益增加的风险，这使得开发者提供关于其安全性的保证变得至关重要。提供此类保证的一种方法是通过安全案例：一种结构化的、基于证据的论证，旨在证明与安全关键系统相关的风险为何是可接受的。在本文中，我们提出了一种针对攻击性网络能力的安全案例模板。我们说明了开发者如何通过将主要主张分解为逐步具体的子主张（每个子主张都有证据支持），来论证模型不具备构成不可接受网络风险的能力。在我们的模板中，我们识别了多种风险模型，从风险模型中推导出代理任务，为代理任务定义评估设置，并将这些与评估结果联系起来。当前前沿安全技术的要素——如风险模型、代理任务和能力评估——使用隐式论证来评估整体系统安全性。此安全案例模板使用主张、论证、证据（CAE）框架整合这些要素，以使安全论证连贯且明确。尽管关于具体细节仍存在不确定性，但该模板作为概念验证，旨在促进关于AI安全案例的讨论并推进AI保证。
英文摘要：
Frontier artificial intelligence (AI) systems pose increasing risks to society, making it essential for developers to provide assurances about their safety. One approach to offering such assurances is through a safety case: a structured, evidence-based argument aimed at demonstrating why the risk associated with a safety-critical system is acceptable. In this article, we propose a safety case template for offensive cyber capabilities. We illustrate how developers could argue that a model does not have capabilities posing unacceptable cyber risks by breaking down the main claim into progressively specific sub-claims, each supported by evidence. In our template, we identify a number of risk models, derive proxy tasks from the risk models, define evaluation settings for the proxy tasks, and connect those with evaluation results. Elements of current frontier safety techniques - such as risk models, proxy tasks, and capability evaluations - use implicit arguments for overall system safety. This safety case template integrates these elements using the Claims Arguments Evidence (CAE) framework in order to make safety arguments coherent and explicit. While uncertainties around the specifics remain, this template serves as a proof of concept, aiming to foster discussion on AI safety cases and advance AI assurance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
