对大型视觉语言模型的攻击调查:资源,进展和未来趋势
IEEE transactions on neural networks and learning systems
|August 18, 2025
概括
大视觉语言模型 (LVLMs) 显示出很大的前景,但有未充分探索的漏洞. 本综述详细介绍了当前的LVLM攻击,包括对抗性,越狱,即时注入和数据中毒,以增强安全性.
科学领域:
- 人工智能的人工智能
- 计算机科学 计算机科学
- 网络安全 网络安全
背景情况:
- 大视觉语言模型 (LVLMs) 是先进的AI系统,在多式联络任务中具有越来越大的能力.
- 尽管它们具有潜力,但LVLM的安全漏洞仍然在很大程度上未经审查,构成风险.
- 目前对LVLM安全性的研究是有限的,需要一个全面的概述.
研究的目的:
- 提供对目前针对LVLM的攻击方法的彻底审查.
- 为了分类和分析不同类型的LVLM攻击.
- 确定LVLM安全和安全的未来研究方向.
主要方法:
- 对LVLM攻击研究的系统文献综述.
- 将攻击分类为对抗性,越狱,即时注入和数据中毒.
- 分析攻击预备,挑战和资源.
主要成果:
- 确定并详细说明了各种LVLM攻击载体,包括对抗性攻击,越狱,即时注入和数据中毒.
- 强调了这些攻击的日益复杂性和潜在影响.
- 建立了对当前LVLM威胁格局的基本理解.
结论:
- LVLM 漏洞是一个显著且日益增长的安全问题.
- 进一步的研究对于开发针对LVLM攻击的有效缓解策略至关重要.
- 本次调查旨在指导未来在保护LVLM应用程序方面的努力.
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