Open-source alternatives to Cleverhans

PurpleLlama is a collection of security components and toolkits designed for large language models. It provides specialized systems including a code security scanner, a content moderation system, a prompt injection firewall, and a security assessment toolkit. The project enables the identification and blocking of jailbreaking attempts and malicious prompts during model inference. It includes capabilities for detecting violating content across multiple languages and modalities and scanning generated code for vulnerabilities to prevent the execution of insecure commands. The framework further

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

This project is a comprehensive educational resource and technical manual focused on interpretable machine learning and explainable AI. It serves as a textbook and reference for implementing techniques that make complex machine learning models transparent and understandable to humans. The resource provides guidance on both building inherently transparent models, such as decision trees and sparse linear models, and applying post-hoc explanation methods to black-box systems. It details specific methodologies for quantifying feature importance, generating rationales for individual predictions, a

This project is a comprehensive ecosystem of frameworks, toolkits, and datasets designed to evaluate model vulnerabilities and analyze jailbreak patterns. It serves as an adversarial testing framework and research toolkit for measuring the effectiveness of safety guardrails in large language models. The system includes a library of real-world prompt injection datasets harvested from social media to study bypass strategies. It provides specialized tools for semantic attack analysis and prompt visualization, allowing for the mapping of relationships between adversarial prompts to discover commo

AI-Infra-Guard is a security scanning platform designed to detect vulnerabilities across large language model deployments, AI agent skills, and the underlying infrastructure. It functions as a security toolset for auditing source code, evaluating model robustness, and identifying insecure network configurations. The project provides a red teaming framework that uses curated attack datasets to test for jailbreak vulnerabilities and prompt injections. It also includes an infrastructure auditor that employs network fingerprinting and asset discovery to match running components against known comm

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

MegEngine is a deep learning framework and automatic differentiation engine used for training and deploying neural networks. It functions as a differentiable programming library that enables the creation of mathematical models where operations are differentiable for gradient-based optimization. The project provides a hardware-agnostic tensor runtime and cross-platform model runtime, allowing models to execute across diverse CPU and GPU hardware architectures. It utilizes a dynamic computational graph engine to build execution graphs on the fly, supporting flexible input shapes and complex con

Gorgonia is a Go library that provides an automatic differentiation engine and a computation graph framework for building and training neural networks. It functions as a CUDA-accelerated tensor library and a SIMD-optimized math library, enabling machine learning workflows entirely within the Go ecosystem. The library distinguishes itself through a dual-backend architecture that dispatches neural network operations to either a GPU or CPU depending on CUDA availability at runtime. It constructs differentiable directed acyclic graphs of tensor operations, supports reverse-mode automatic gradient

tiny-dnn is a header-only C++14 deep learning framework for building, training, and running inference on neural networks. It constructs static computational graphs at compile time using template-based layer composition, with a gradient-based backpropagation engine and minibatch stochastic gradient descent for training, all without external dependencies beyond the C++14 standard library. The framework supports importing pre-trained models from the Caffe framework directly, parsing its binary serialization format without requiring external protocol buffer libraries. It provides CPU-optimized te

OpenCompass is a comprehensive evaluation platform, benchmarking suite, and distributed model evaluator designed to measure the performance and accuracy of large language models. It provides a framework for benchmarking both open-source and API-based models against diverse datasets using standardized metrics and reproducible pipelines. The project features an automated judging framework that uses language models as judges to score and verify the quality of generated text. It includes a performance leaderboard system for comparing the relative capabilities of various models across industry-sta

This project provides a comprehensive educational curriculum and research resource for deep learning, focusing on the theoretical and technical foundations of neural network implementation. It serves as a structured academic guide for building and training complex models from scratch, covering the essential mathematical primitives, computational graph construction, and automatic differentiation mechanisms required for modern machine learning. The repository distinguishes itself through its extensive coverage of generative modeling and specialized neural architectures. It includes practical im

Evidently is an AI observability platform and evaluation framework designed to quantify the performance of machine learning models and large language models. It functions as a monitoring tool for detecting data drift and quality degradation in tabular datasets, while providing a specialized analyzer for the faithfulness and correctness of retrieval augmented generation systems. The project distinguishes itself through an evaluation framework that utilizes judge models and custom rubrics to score language model outputs. It includes tools for iterative prompt optimization and the generation of

mcp-context-forge is a Model Context Protocol federation gateway that unifies diverse AI tool servers and APIs into a single consistent interface for discovery and execution. It acts as a centralized proxy that aggregates multiple servers and APIs, allowing AI agents to access and invoke a unified set of tools, prompts, and resources. The project distinguishes itself through a multi-protocol translation bridge that converts communication between standard I/O, SSE, gRPC, and REST to enable interoperability between disparate tool servers. It includes a comprehensive LLM evaluation framework for

Nerfstudio is a modular development framework for training, visualizing, and exporting three-dimensional scene representations derived from two-dimensional image datasets. It provides a neural scene reconstruction pipeline that converts raw images and camera data into high-fidelity 3D assets and cinematic video using a differentiable volumetric renderer. The system features an interactive web-based visualizer that allows users to monitor training progress and inspect neural scene geometry in real time. It decouples neural network architectures from the training loop through a standardized mod

Fawkes is an adversarial image generator and facial recognition cloaking tool designed to protect privacy by obfuscating facial features in photos. It functions as an image privacy obfuscator that adds invisible pixel perturbations to images, preventing facial recognition models from accurately identifying a person while keeping the image visually clear to humans. The system employs adversarial perturbation mapping and feature-space obfuscation to mislead machine learning classifiers. By utilizing an iterative optimization loop and model-agnostic noise generation, it modifies facial represent

A pytorch implementation of Paper "Improved Training of Wasserstein GANs"

Bullet3 is a professional physics simulation engine designed for calculating rigid body, soft body, and collision dynamics within 3D environments and robotics applications. It functions as a computational framework for determining complex geometric intersections and contact manifolds between objects in simulated space. The library distinguishes itself through a distributed rendering framework that scales heavy graphical workloads and scene generation tasks across large clusters of machines. This capability enables the production of massive datasets by distributing complex scene generation acr

This project is a generative adversarial network designed for image animation and motion transfer. It functions as a computer vision framework that synthesizes video sequences by applying motion patterns extracted from a driving video onto a static source image. The model distinguishes itself by using a keypoint-based representation to decouple object appearance from temporal movement. By tracking structural deformations through learned latent coordinates, it performs motion retargeting and synthetic media production without requiring manual annotations or object-specific training data. The

Openface is a deep learning toolkit designed for facial recognition and identity verification. It provides a comprehensive pipeline for detecting faces, aligning landmarks, and transforming facial images into compact numerical vectors. By utilizing these embeddings, the system enables identity classification and similarity comparison through geometric distance calculations. The project distinguishes itself by integrating research-oriented diagnostic tools alongside its core recognition capabilities. It includes utilities for visualizing high-dimensional feature clusters, inspecting internal c

OpenPose is a real-time pose estimation engine designed to detect and track human body, face, hand, and foot landmarks. It functions as a multi-person motion tracker, identifying the spatial coordinates of multiple individuals simultaneously within video streams or static images. Beyond two-dimensional detection, the software acts as a three-dimensional kinematics processor, reconstructing spatial movement data from single or multiple synchronized camera perspectives. The system distinguishes itself through a bottom-up approach that utilizes part-affinity fields to associate body parts across

Open source person re-identification library in python

The open-source agent firewall Prevent AI agents from leaking data, using dangerous tools, and importing poisoned dependencies. AI made bug discovery cheap. ClawMoat helps you contain the blast radius while the patch queue catches up.

FaceNet is a facial recognition framework designed to transform facial images into high-dimensional numerical embeddings for identity verification and recognition. It provides a deep learning face embedder that maps facial features into a Euclidean space where distance corresponds to facial similarity. The system includes tools for both supervised and unsupervised identity management. It features a face identity classifier for categorizing images into known identity classes and an unsupervised clustering tool to group similar facial embeddings together without predefined labels. The framewor

Convolutional Recurrent Neural Network