garak is an open-source LLM vulnerability scanner developed by NVIDIA that probes large language models for security weaknesses including prompt injection, jailbreaks, hallucination, toxicity, data leakage, and misinformation.

OpenEnv is a framework for evaluating AI agents against real systems rather than simulations. It provides a standardised way to connect agents to real tools and workflows while preserving the structure needed for consistent and reliable evaluation.

The AI Inherent Risk Scale (AIIRS) is a task-based classification instrument that helps organisations assess the inherent risk of generative AI use. It evaluates tasks against three criteria—epistemic dependence, verifiability, and consequences of error—and assigns a LOW, MEDIUM, or HIGH risk rating using a max-dominant model. AIIRS supports proportionate safeguards, accountable oversight, and governance-aligned decision-making without determining whether AI use is permitted in a given context.

The Approved Intelligence Platform (AIP) provides modular, scenario-based testing workflows to evaluate mission-critical AI systems in defence, public safety, and critical civil use cases. It delivers a comprehensive, end-to-end testing environment based on a proprietary AI trust ontology with measurable AI Solutions Quality Indicators (ASQI) for the testing, evaluation, validation and verification of software solutions with different AI modalities.

AI Screener to enable universal early screening for all children.

BeSpecial is an AI-driven platform designed to support university students with dyslexia by providing personalized digital tools and tailored learning strategies. Developed within the European VRAILEXIA project, BeSpecial combines clinical data, self-assessments, and psychometric tests to recommend customized resources like audiobooks and concept maps, as well as inclusive academic practices. The platform also raises awareness and trains educators to foster inclusive higher education environments.

FloodMapp is a technology company that specialises in rapid real-time flood forecasting and flood inundation mapping to provide greater warning time and situational awareness.

SeismicAI is a provider of innovative Earthquake Early Warning Systems (EEW) ensuring earthquake preparedness. SeismicAI's algorithms utilise local sensors to issue high-precision alerts for earthquake preparedness. The system covers the full early warning cycle - from monitoring and reporting, through alerts, to optionally triggering automated preventive actions.

The AIFS is the first fully operational weather prediction open model using machine learning technology for weather forecasting.

The GDA leverages aerial imagery, satellite data, and machine learning techniques to evaluate the damage in areas impacted by natural disasters. This tool greatly enhances the efficiency and precision of disaster response operations.

ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) is a globally accessible, living knowledge base of adversary tactics and techniques against Al-enabled systems based on real-world attack observations and realistic demonstrations from Al red teams and security groups.

This program provides organisations with a comprehensive, independent review of their AI approaches, ensuring alignment with consensus standards and enhancing trust among stakeholders and the public in their AI practices.

MLPerf Client is a benchmark for Windows and macOS, focusing on client form factors in ML inference scenarios like AI chatbots, image classification, etc. The benchmark evaluates performance across different hardware and software configurations, providing command line interface.

This document addresses how artificial intelligence machine learning can impact the safety of machinery and machinery systems.

ISO/IEC 25023:2016 defines quality measures for quantitatively evaluating system and software product quality in terms of characteristics and subcharacteristics defined in ISO/IEC 25010 and is intended to be used together with ISO/IEC 25010.

PLIM is designed to make benchmarking and continuous monitoring of LLMs safer and more fit for purpose. This is particularly important in high-risk environments, e.g. healthcare, finance, insurance and defence. Having community-based prompts to validate models as fit for purpose is safer in a world where LLMs are not static. 

AIxploit is a tool designed to evaluate and enhance the robustness of Large Language Models (LLMs) through adversarial testing. This tool simulates various attack scenarios to identify vulnerabilities and weaknesses in LLMs, ensuring they are more resilient and reliable in real-world applications.

Continuous proactive AI red teaming platform for AI and GenAI models, applications and agents.

Continuous automated red teaming for AI, minimize security threats to AI models and applications.

Evaluating machine learning agents on machine learning engineering.