合作透测试套件用于新兴的生成性AI算法
Petar Radanliev1,2,3
1Department of Computer Science, University of Oxford, Wolfson Building, Parks Rd, OX1 3QG Oxford, England.
概括
这项研究引入了一套新的透测试套件,以保护生成AI免受量子威胁. 它有效地识别和修复漏洞,增强整体系统安全性.
科学领域:
- 网络安全 网络安全
- 量子计算是一种量子计算.
- 人工智能的人工智能
背景情况:
- 生成型人工智能系统面临着重大网络威胁和量子计算挑战.
- 现有的安全措施不足以抵御先进的对抗性和量子辅助攻击.
研究的目的:
- 提出和评估一套新的透测试套件,以保护生成AI免受量子安全问题的威胁.
- 整合先进的安全测试方法和量子抗性协议.
主要方法:
- 使用OWASP ZAP,Burp Suite,SonarQube和Fortify.com进行动态和静态应用程序安全测试 (DAST和SAST) 的集成.
- 通过互动应用安全测试 (IAST) 与对比评估进行实时监控.
- 使用Hyperledger Fabric进行区块链增强的日志记录,以防改记录.
- 实施量子抗性加密协议 (基于格子的,RLWE).
- 用AI驱动的红色团队模拟用于对抗和量子辅助攻击模拟.
主要成果:
- 识别和修复300多个漏洞,在两周内减少70%的严重问题.
- 通过区块链记录的漏洞的解决效率为90%.
- 证明了量子抗性协议对模拟量子攻击的弹性.
- 成功安全的API加密和数据传输.
结论:
- 开发的透测试套件建立了一个新协议,用于保护生成性AI系统.
- 综合方法有效地解决了传统和量子相关的安全漏洞.
- 这些发现强调了在人工智能和量子计算时代主动和先进安全措施的重要性.
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