Keyboard shortcuts

Press ← or → to navigate between chapters

Press S or / to search in the book

Press ? to show this help

Press Esc to hide this help

ProofPoint 邮件防护规避 (AML.CS0008)

一句话通俗理解

先克隆一个 ProofPoint 的邮件防护模型,再对着副本打磨绕过手法——原版应声失效。

案例速览

维度内容
案例类型演练 / 安全研究
发生时间2019-09-09
攻击者Researchers at Silent Break Security
受害目标ProofPoint Email Protection System

事件经过

Proof Pudding(CVE-2019-20634)研究展示了 ML 研究者如何规避 ProofPoint 邮件防护系统:先构建一个模仿其行为的本地模型,再针对该模型寻找规避样本,最终成功欺骗了真实的 ProofPoint 系统。

运用的 ATLAS 技术

以下攻击链映射来自 MITRE ATLAS 官方数据(AML.CS0008 的 employs 关系),按官方步骤编号排序;技术名称链接至本库对应技术文档,引用文字为官方对本次运用的描述。

S00

S01

S02

Basic correlation was used to decide which score variable speaks generally about the security of an email. The “mlxlogscore” was selected in this case due to its relationship with spam, phish, and core mlx and was used as the label. Each “mlxlogscore” was generally between 1 and 999 (higher score = safer sample). Training was performed using an Artificial Neural Network (ANN) and Bag of Words tokenizing.

S03

  • 黑盒迁移 (AML.T0043.002)

    Next, the ML researchers algorithmically found samples from this “offline” proxy model that helped give desired insight into its behavior and influential variables.

Examples of good scoring samples include “calculation”, “asset”, and “tyson”. Examples of bad scoring samples include “software”, “99”, and “unsub”.

S04

  • 规避 AI 模型 (AML.T0015)

    Finally, these insights from the “offline” proxy model allowed the researchers to create malicious emails that received preferable scores from the real ProofPoint email protection system, hence bypassing it.

关联战术

参考资料

以下为 MITRE ATLAS 官方案例收录的参考来源:


版本: ATLAS 2026.09 | 更新: 2026-10-24 | 模板: ATLAS 案例研究 v1.0