
PyRIT
Open-source framework for red-teaming generative AI systems: automate attack prompts, score model responses, and audit behavior to identify security…

Open-source framework for red-teaming generative AI systems: automate attack prompts, score model responses, and audit behavior to identify security…

Streaming machine learning library for incremental learning on data streams, providing online estimators, drift and anomaly detection, pipelines,…

Python library for adversarial machine learning security, enabling red and blue teams to run evasion, poisoning, extraction, and inference attacks…

An adversarial example library for constructing attacks, building defenses, and benchmarking both

Source code about machine learning and security.

Scalable Python library for time series analysis via matrix profiles, enabling motif discovery, anomaly detection, semantic segmentation, and…

The community's most comprehensive, continuously-updated index of research on Large Language Models for software vulnerability detection — papers…

Curated systematic literature review of 756+ papers on LLM applications in cybersecurity, covering threat intelligence, vulnerability detection,…

Self-hostable AI SOC that fuses security alerts, auto-triages via agentic AI, runs MITRE ATT&CK investigations, and logs every agent decision in a…

Bidirectional token-classification model for PII detection and masking in text, with CLI for redaction, evaluation, and finetuning on-premises.

Interpretability and explainability of data and machine learning models

A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in…

AI-powered threat intelligence platform for automated CVE/ransomware monitoring, domain surveillance, data leak detection, and incident response with…

A unified framework for privacy-preserving data analysis and machine learning

Interactive dashboards and libraries for responsible AI model debugging, covering error analysis, fairness, interpretability, counterfactuals, causal…

A Python toolbox to create adversarial examples that fool neural networks in PyTorch, TensorFlow, and JAX

An experimentation and research platform to investigate the interaction of automated agents in an abstract simulated network environments.

Algorithms for outlier, adversarial and drift detection