FLARE-AI is an open-source AI flaw and incident reporting platform that lets anyone document a flaw in any AI system and route a single, standardised report to multiple developers and coordinators at once. It enables any AI actor to document vulnerabilities, biases, or incidents and route a single, standardised (JSON-LD) report to multiple developers, coordinators, and registries in the ecosystem.

The REFRAIME Legal Toolkit provides legal practitioners, public authorities, and civil society organisations with a structured resource for identifying and addressing the impact of AI systems on fundamental rights under the EU Charter. Developed by a consortium including the Center for the Study of Democracy, the European Center for Not-for-Profit Law, and the University of Malta, and co-funded by the European Union, the toolkit combines knowledge articles, sixteen real-world case studies grounded in actual case law (including ACLU v. Clearview AI, SCHUFA before the CJEU, and the Dutch Childcare Benefits case), an interactive glossary, a curated directory of EU, Council of Europe, UN, and OECD instruments, and a 43-point checklist for monitoring compliance with Fundamental Rights Impact Assessment obligations under Article 27 of the EU AI Act.

The AIM Framework (Awareness, Identification, Mitigation) presents a stepwise approach for the implementation of risk management strategies. The framework is intended for AI developers working in private, academic, or public sectors. It features a checklist with indicative scenarios for awareness-raising and training purposes.

SafeIMG is a safety-oriented benchmark for evaluating synthetic-image detection and visual-evidence verification in high-risk public- and personal-safety scenarios. It provides scenario-specific synthetic images generated for assessing how misleading visuals could impact safety and accountability. Researchers and developers can use it to verify and compare image authenticity methods, and to improve robustness of safeguards for trustworthy visual content.

ModelBench is a benchmarking tool for running safety-focused evaluations on AI models and producing detailed reports on performance against the benchmark suite. It is intended for developers and researchers who need to verify and compare safety/robustness behavior before deployment and during ongoing evaluation. The outputs support auditing and model validation for safety-related trustworthiness objectives.

OpenART is an open-source framework designed to evaluate the security of AI agents in dynamic, long-horizon, and stateful environments. It stress-tests agent runtimes against multi-step state poisoning, privilege escalation, and tool-use vulnerabilities across more than 10,000 benchmark scenarios.

IndicSafeEval is a multilingual benchmark framework for evaluating the safety and robustness of large language models (LLMs) against persuasion-based jailbreak attacks in Indian languages. It combines safety-critical content categories with multiple human-like persuasion strategies and evaluates model responses across several languages. The framework is designed to identify safety and alignment failures in non-English settings and can be used to assess and compare model behavior, support model validation, and monitor safety performance.

The AI Slop Side Effect Database documents indirect harms to legitimate users, creators, researchers, and organisations caused by the proliferation of low-quality AI-generated content and by countermeasures introduced to control it. It classifies cases across gatekeeping failures, content contamination, discriminatory bias, institutional invisibility, and service self-contamination, with evidence levels, affected parties, sources, and analytical commentary.

Ster is an open-source framework for intervening in large language model behavior at the level of internal activations to reduce harmful outputs and hallucinations. It helps users improve safety and robustness by enabling targeted controls that affect what the model produces, supporting transparency by making activation-level intervention mechanisms available for inspection and explanation.

Human Approval Gate is a free, platform-neutral educational kit that helps leaders, educators, operators, and small teams define what a qualified person must check before AI-assisted work can affect a real decision or action. It includes a practical guide, printable worksheet, facilitator notes, and ten synthetic test cases. Its CLEAR test holds the consequence, names the reviewer and evidence, preserves accept, revise, reject, and escalate outcomes, and records the decision and recovery path.

GovAI is the official Australian Government AI platform. Announced in July 2025, GovAI offers Australian Public Service (APS) staff secure access to advanced AI capabilities through government-controlled infrastructure. A key aspect of the platform is the Interactive Learning Environment that provides hands-on-training using multiple AI models, enabling staff to build practical AI skills in a secure, risk-free environment.

Provael is an open source tool that red teams vision language action policies, the models that convert camera input and instructions into physical robot actions, by running adversarial attacks in simulation and reporting an attack success rate with a statistical confidence interval and a benign control. Results are issued as machine readable evidence, mapped to an independently authored embodied AI security taxonomy and cross referenced to current regulatory frameworks including the EU AI Act, the EU Machinery Regulation, and ISO 10218. The full testing functionality is available at no cost under the Apache 2.0 licence, and the project publishes, alongside its findings, an explicit account of which attack families have been validated against real models and which remain unvalidated.

Microsoft Agent Governance Toolkit is an open-source framework for governing AI agents at scale. The toolkit provides identity management, policy enforcement, authorization, and audit capabilities, helping organizations control agent actions and maintain accountability across agent workflows and enterprise systems.

A three-layer, enterprise-wide AI risk management and governance framework that operationalises trustworthy AI from board strategy to operational controls and organisational resilience.

Web application that allows organizations to assess their level of maturity in artificial intelligence governance and automatically generate a customized roadmap to meet national and international standards

Cloud platform for evaluating AI system performance on private data and presenting results

Amazon Nova Premier is a multimodal foundation model that was evaluated under Amazon’s Frontier Model Safety Framework to assess and mitigate risks related to Chemical, Biological, Radiological, and Nuclear (CBRN) weapons proliferation, offensive cyber operations, and automated AI research and development.

Lightweight AI safety auditing framework for red-teaming AI systems through adversarial probing. Supports multilingual testing across safety, healthcare, and RAG scenarios, and works with cloud APIs or fully local models.

The AI red team service exposes hidden safety and security threats across the entire lifecycle of artificial intelligence (AI) systems by applying an adversarial mindset to assess AI systems during design, development, deployment, and operations stages.

FlowMS is an AI-powered utility efficiency tool built on AWS that analyses metering data to detect anomalies in water use and help conserve water in Amazon buildings.