整合多种安全工具，构建复合型AI安全分析框架
BlueGlass: A Framework for Composite AI Safety
- 统一架构集成多类安全工具，覆盖模型内外部
- 发现视觉语言模型在不同数据分布下的性能权衡与失效模式
- 适用于需要系统性安全评估的AI研发团队
随着AI系统能力日益增强且广泛应用，确保其安全性至关重要。然而，现有安全工具通常针对模型安全的不同方面，单独使用难以提供全面保障，亟需集成化、复合化的方法。本文提出BlueGlass框架，通过统一基础设施实现对跨模型内部与输出的安全工具的集成与组合，支持复合式AI安全工作流。为验证该框架有效性，我们针对视觉语言模型在目标检测任务中开展三项安全分析：(1) 分布评估揭示了不同数据分布下的性能权衡与潜在失效模式；(2) 基于探针的层动态分析揭示了层次化学习中的相变现象；(3) 稀疏自编码器识别出可解释的概念。本工作为构建更鲁棒、可靠的AI系统提供了基础架构与关键发现。
原文摘要 · Abstract (English)
As AI systems become increasingly capable and ubiquitous, ensuring the safety of these systems is critical. However, existing safety tools often target different aspects of model safety and cannot provide full assurance in isolation, highlighting a need for integrated and composite methodologies. This paper introduces BlueGlass, a framework designed to facilitate composite AI safety workflows by providing a unified infrastructure enabling the integration and composition of diverse safety tools that operate across model internals and outputs. Furthermore, to demonstrate the utility of this framework, we present three safety-oriented analyses on vision-language models for the task of object detection: (1) distributional evaluation, revealing performance trade-offs and potential failure modes across distributions; (2) probe-based analysis of layer dynamics highlighting shared hierarchical learning via phase transition; and (3) sparse autoencoders identifying interpretable concepts. More broadly, this work contributes foundational infrastructure and findings for building more robust and reliable AI systems.
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