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Cleverhans is a TensorFlow adversarial machine learning library that serves as an attack framework, a robustness benchmark, and a defense library. It provides a collection of tools to generate adversarial examples, test the security of neural networks, and implement protective mechanisms to increase model resilience against malicious inputs.
The main features of tensorflow/cleverhans are: Adversarial Frameworks, Gradient-Based Perturbations, Adversarial Robustness Testing, Adversarial Robustness Libraries, Defense Libraries, Adversarial Input Generation, Adversarial Threat Defenses, Automatic Differentiation Engines.
Open-source alternatives to tensorflow/cleverhans include: cleverhans-lab/cleverhans — Cleverhans is an adversarial machine learning library and toolkit designed to generate adversarial examples,… trusted-ai/adversarial-robustness-toolbox — The Adversarial Robustness Toolbox (ART) is an open-source library that provides a unified framework for evaluating,… llm-attacks/llm-attacks — This repository provides tools and methodologies for studying adversarial attacks on large language models. It focuses… giskard-ai/giskard — Giskard is an evaluation framework, testing library, and quality monitoring system for large language models and AI… meta-llama/purplellama — PurpleLlama is a collection of security components and toolkits designed for large language models. It provides… christophm/interpretable-ml-book — This project is a comprehensive educational resource and technical manual focused on interpretable machine learning…
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
The Adversarial Robustness Toolbox (ART) is an open-source library that provides a unified framework for evaluating, defending, and certifying machine learning models against adversarial threats. It wraps models from any framework behind a common estimator interface, enabling composable pipelines for attack generation, defense application, robustness certification, and privacy auditing across evasion, poisoning, and extraction threats. The library distinguishes itself by covering the full adversarial ML security lifecycle within a single toolkit. It supports gradient-based adversarial example
This repository provides tools and methodologies for studying adversarial attacks on large language models. It focuses on understanding how carefully crafted inputs can manipulate or bypass the safety mechanisms of LLMs, enabling researchers to probe model vulnerabilities and improve their robustness. The project covers techniques for generating adversarial prompts, evaluating model responses under attack conditions, and analyzing the effectiveness of different attack strategies.
Giskard is an evaluation framework, testing library, and quality monitoring system for large language models and AI agents. It serves as a toolkit for quantifying model performance and reliability, providing specialized capabilities for validating retrieval-augmented generation pipelines. The project distinguishes itself through an automated red teaming tool and security scanner designed to identify vulnerabilities, prompt injections, and safety risks. It utilizes adversarial probing and synthetic edge case generation to quantify model robustness and detect information disclosure. The platfo