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Microsoft Counterfit

Rating:

3.3 / 5.0

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Microsoft Counterfit

Tags

AI security, adversarial testing, machine learning, open-source, penetration testing

Pricing Details

Free, open-source. Available via GitHub. Support via Azure may incur cloud resource costs.

Features

Automated adversarial attacks, penetration testing, vulnerability scanning, attack logging, environment-agnostic, model-agnostic, data-agnostic, integration with ART and TextAttack

Integrations

Integrates with Azure, Adversarial Robustness Toolbox, TextAttack, Metasploit, Python libraries

Preview

Microsoft Counterfit, released as an open-source project in May 2021, is a command-line tool designed to enhance the security of AI and machine learning systems by automating adversarial attack testing. Born from Microsoft’s need to secure its own AI services, it evolved from a set of attack scripts into a generic automation layer capable of targeting multiple AI systems at scale. Counterfit supports security professionals in penetration testing, vulnerability scanning, and logging attacks, using preloaded algorithms from frameworks like Adversarial Robustness Toolbox and TextAttack. Its environment, model, and data-agnostic design allows it to assess AI models hosted in cloud, on-premises, or edge environments, handling diverse inputs like text and images. The tool’s workflows mirror popular offensive tools like Metasploit, making it accessible to security teams. Counterfit has been tested with partners like Airbus, demonstrating its value in industries such as healthcare, finance, and defense. Recent updates in 2023 improved its integration with Azure, enhancing deployment via Azure Shell and Container Instances. With over 1K GitHub stars, it fosters community contributions and aligns with Microsoft’s Responsible AI principles, helping organizations meet regulatory requirements like GDPR. A Microsoft survey of 28 businesses revealed 25 lacked adequate AI security tools, underscoring Counterfit’s role in addressing this gap. Tutorials and live webinars, such as the May 2021 session, aid adoption, making it a critical tool for securing AI against adversarial threats.