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一款关于Shiro 1.2.4反序列化漏洞利用的回显工具。最新版本为v2.4。

What a journey it has been. I wrote quite a bit about machine learning from a red teaming/security testing perspective this year. It was brought to my attention to provide a conveninent “index page” with all Husky AI and related blog posts. Here it is.

• Getting the hang of machine learning
• The machine learning pipeline and attacks
• Husky AI: Building a machine learning system
• MLOps - Operationalizing the machine learning model

• Threat modeling a machine learning system
• Grayhat Red Team Village Video: Building and breaking a machine learning system
• Assume Bias and Responsible AI

• Brute forcing images to find incorrect predictions
• Smart brute forcing
• Perturbations to misclassify existing images
• Adversarial Robustness Toolbox Basics
• Image Scaling Attacks
• Stealing a model file: Attacker gains read access to the model
• Backdooring models: Attacker modifies persisted model file
• Repudiation Threat and Auditing: Catching modifications and unauthorized access
• Attacker modifies Jupyter Notebook file to insert a backdoor
• CVE 2020-16977: VS Code Python Extension Remote Code Execution
• Using Generative Adversarial Networks (GANs) to create fake husky images
• Using Microsoft Counterfit to create adversarial examples
• Backdooring Pickle Files
• Backdooring Keras Model Files and How to Detect It

• Participating in the Microsoft Machine Learning Security Evasion Competition - Bypassing malware models by signing binaries
• Husky AI Github Repo

As you can see there are many machine learning specific attacks, but also a lot of “typical” red teaming techniques that put AI/ML systems at risk. For instance well known attacks such as SSH Agent Hijacking, weak access control and widely exposed credentials will likely help achieve objecives during red teaming operations.

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