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0xRadi/AI-ML-Security-Reading-List

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AI ML Security Reading List

Features

  • AI Security and Red Teaming - Curated reading list for AI/ML security.

Star history

Star history chart for 0xradi/ai-ml-security-reading-listStar history chart for 0xradi/ai-ml-security-reading-list

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

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Projects sharing features with AI ML Security Reading List

These projects share indexed features with AI ML Security Reading List. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • trigaten/learn_promptingtrigaten avatar

    trigaten/Learn_Prompting

    4,709View on GitHub↗

    Learn_Prompting is an educational project focused on prompt engineering, providing the principles and techniques required to craft effective inputs and improve the quality of generative AI outputs. The project covers advanced prompting strategies to enhance reasoning, reliability, and output quality. This includes techniques for task decomposition, chain-of-thought reasoning, and the use of few-shot and zero-shot guidance. It also addresses model security through the study of prompt hacking, vulnerability analysis, and privacy auditing to prevent sensitive data leaks. The scope extends to th

    MDXchatgptchatgpt-apideep-learning
    View on GitHub↗4,709
  • bethgelab/foolboxbethgelab avatar

    bethgelab/foolbox

    2,966View on GitHub↗

    .. raw:: html

    Python
    View on GitHub↗2,966
  • cleverhans-lab/cleverhanscleverhans-lab avatar

    cleverhans-lab/cleverhans

    6,443View on GitHub↗

    Cleverhans is an adversarial machine learning library and toolkit designed to generate adversarial examples, incorporate them into training loops, and benchmark the resilience of machine learning models. It provides a gradient-based attack framework for constructing both white-box and black-box attacks to identify model misclassifications. The project includes capabilities for model robustness benchmarking, allowing users to evaluate and verify how models resist evasion attacks and malicious input perturbations. It also facilitates adversarial training to increase a model's resistance to pert

    Jupyter Notebookbenchmarkingmachine-learningsecurity
    View on GitHub↗6,443
  • azure/pyritAzure avatar

    Azure/PyRIT

    3,444View on GitHub↗

    PyRIT is an AI vulnerability assessment tool and security scanner designed to detect risks in large language model applications. It functions as a generative AI red teaming framework used to simulate adversarial attacks and identify weaknesses in system guardrails. The tool automates AI risk assessment by scanning generative AI components for security vulnerabilities. It utilizes automated testing and analysis to identify security gaps and prevent potential exploits through a consistent, repeatable process. The system incorporates asynchronous model orchestration to compare security postures

    Pythonai-red-teamgenerative-aired-team-tools
    View on GitHub↗3,444
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Frequently asked questions

What are the main features of 0xradi/ai-ml-security-reading-list?

The main features of 0xradi/ai-ml-security-reading-list are: AI Security and Red Teaming.

Which projects share features with 0xradi/ai-ml-security-reading-list?

Projects with overlapping indexed features include: trigaten/learn_prompting — Learn_Prompting is an educational project focused on prompt engineering, providing the principles and techniques… bethgelab/foolbox — .. raw:: html. cleverhans-lab/cleverhans — Cleverhans is an adversarial machine learning library and toolkit designed to generate adversarial examples,… ethiack/ai4eh. evilsocket/audit. azure/pyrit — PyRIT is an AI vulnerability assessment tool and security scanner designed to detect risks in large language model…