v2.2.1

What's Changed

Features & Functionalities:

• Description improvement to Fairness Tree under User Inputs to address issue #634 in #698
• Fix robustness widget display information to address issue #692 by @timlrx in #697

Security Vulnerabilities

• Resolve vulnerabilities from dependencies/docker in #704
  • Update dependency for aiverify-apigw-node and aiverify-shared-library via npm audit
  • Update dependency for aiverify-apigw, aiverify-test-engine and aiverify-test-engine-worker via pip-audit
  • busybox-1.37.0-r30 is auto installed in aiverify-portal docker image to address CVE-2025-46394 mentioned in #701
  • Upgrade urllib3 to address CVE-2025-66418, CVE-2025-66471 and CVE-2026-21441 mentioned in #701
  • Upgrade Nextjs & React to address CVE-2025-55182, CVE-2025-55184, CVE-2025-67779
  • Upgrade Pillow to address CVE-2026-25990
  • Upgrade keras to address CVE-2025-12060, CVE-2025-12638, CVE-2025-12058, CVE-2026-0897, CVE-2026-1669
  • Upgrade Pytorch to address CVE-2025-55552, CVE-2025-55551, CVE-2025-55554, CVE-2025-63396, CVE-2026-24747
  • Upgrade keras to address CVE-2025-12058, CVE-2025-12060, CVE-2026-0897, CVE-2026-1669
  • Upgrade werkzeug to address CVE-2025-66221, CVE-2026-21860, CVE-2026-27199
  • Upgrade protobuf to address CVE-2026-0994
  • Upgrade jaraco-context to address CVE-2026-23949
  • Upgrade uv to address CVE-2025-13327

Documentations

• aiverify-portal README improvement in #700
• Fix GitHub Pages broken links in README under stock-plugins to address issue #692 in #693
• Replaced deprecated docker-compose command with docker command in AIVT2.0 Quick Start Guide in #703

Full Changelog: v2.2.0...2.2.1

v2.2.0

What's Changed

Features & Functionalities:

• Increase Results Editor upload size limit in Tests Result from 1mb to 20mb
• Add Back Button icon on Input Page to replace clicking on Input ID to go back to parent page
• Make Fairness Tree User Input optional for all reports and show "No Test Result" message in Fairness Tree Widgets in report when Fairness Tree User Input is not selected.

Third-Party Plugins:

• Updated wording for CCCS plugins + new factsheet by @mhfren-sg
• Changed CCCS to CCS for the report's logo, factsheet, process checklists, and quickstart guide by @mhfren-sg

Documentations:

• Added example Technical Test Table for Stock Plugins README
• Added instructions for running Portal, APIGW and Test Engine Worker Venv Profile in Docker-Compose-Dev deployment README
• Added Developer Guide link to root README

Full Changelog: v2.1.0...v2.2.0

v2.1.0

What's Changed

Added Various Features & Functionalities

• Added TensorFlow support to pipeline models @jetkan-yk
• CCCS's AIM toolkit @mhfren-sg
• Kubernetes deployment to run algorithms using pods/jobs @dsta-siminong
• Unify model and pipeline behavior when running predict on a list of data Unify model and pipeline behavior when running predict on a list of data @jetkan-yk

Added Various Fixes:

• Various Bugs Fixes
• Patching Security Vulnerabilities
• CI/CD Pipeline Issues
• Deployment Pipeline Issues
• Security Pipeline Issues

Added Various Unit Tests and Integration Tests:

• Smoke Test
• Integration Test
• APIGW Unit Test
• Portal Unit Test
• Test Engine Unit Test
• Test Engine Worker Unit Test

Full Changelog: v2.0.0...v2.1.0

v2.0.0

What's Changed

• Fix CI/CD for forked repo by @berrydenhartog in #282
• add devcontainer by @berrydenhartog in #258
• improve environment variable inheritance by @berrydenhartog in #267
• • For portal and apigw, omit dev dependencies when npm install. by @imda-kwokwk in #285
• Change IMDA-BTG references in GHA yml files to aiverify-foundation to align with org name change. by @imda-kwokwk in #289
• Feature/update process checklist by @imda-peckyoke in #287
• add base averify-apigw for aiv2 by @imda-peckyoke in #303
• Refactor test engine core by @timlrx in #304
• Fix test-engine-core unit tests and cleanup module by @timlrx in #305
• Implement uploads test results by @imda-peckyoke in #314
• Add support for testing files remotely by @timlrx in #317
• Refactor Blackduck for v2.x by @imda-benedictlee in #316
• Refactor accumulated local effect plugin by @iamksuresh in #307
• Refactor fairness metrics toolbox for classification by @iamksuresh in #313
• refactoring fairness-metrics-for-regression by @iamksuresh in #312
• Refactor image corruption toolbox by @iamksuresh in #319
• Refactor partial dependency plot by @iamksuresh in #320
• Robustness plugin by @iamksuresh in #321
• refactor shap-ttolbar and add support for remote plugin by @iamksuresh in #323
• Update dependencies libraries, test files and pyproject.toml by @timlrx in #325
• Support url for input asset paths by @iamksuresh in #327
• remove obseleted ai-verify-apigw project folder by @imda-peckyoke in #330
• Feature/apigw test results route by @imda-peckyoke in #329
• Update APIGW readme and change DB default URI by @imda-peckyoke in #332
• V2 cleanup by @timlrx in #334
• Update description and version to v2.0.0a by @timlrx in #336
• Update readme and change version of apigw to 2.0.0a1 by @imda-peckyoke in #337
• UPDATE NOTICE.md by @imda-benedictlee in #335
• Add on points about veritas and CV tests to v2.x new features by @imda-peckyoke in #338
• Match plugins output to testresult schema by @iamksuresh in #333
• Support nested artifact filepath and updated schema and misc fixes by @imda-peckyoke in #339
• Dockerfile fairness metrics toolbox for classification by @dsta-siminong in #345
• Update README.md by @HanFai-IMDA in #350
• Dockerfile for Fairness Metrics Toolbox for Regression (#347) by @dsta-siminong in #348
• Dockerfile for Partial Dependence Plot (#353) by @dsta-siminong in #354
• Dockerfile for Accumulated Local Effect (#352) by @dsta-siminong in #355
• Dockerfile for Robustness Toolbox (#357) by @dsta-siminong in #358
• Dockerfiles for Image Corruption Toolbox (#359) by @dsta-siminong in #360
• Dockerfile for SHAP Toolbox (#361) by @dsta-siminong in #362
• Implement plugin router by @imda-peckyoke in #367
• Portal project setup by @imda-amdlahir in #368
• Create new pypi build for 2.0.0a1 by @timlrx in #364
• Refactor SCA Scan for APIGW by @imda-benedictlee in #356
• Made changes to README/Dockerfile by @dsta-siminong in #371
• Add validation of widgets, input blocks and templates during plugin scans by @imda-peckyoke in #373
• Remove requirements.txt for SHAP ToolBox by @imda-benedictlee in #375
• remove redis from test engine by @timlrx in #380
• Feature/apigw projects router by @imda-peckyoke in #381
• Test Results Page by @wanfei-imda in #374
• Add aiverify-portal folder in Blackduck Scan by @imda-benedictlee in #379
• Minor docker fix (#390) by @dsta-siminong in #391
• 1. update name and description lengths 2) add modeltyle uplift by @imda-peckyoke in #392
• Add Changelog by @imda-peckyoke in #393
• Veritas toolkit migration by @timlrx in #369
• fix re expression for matching filename by @imda-peckyoke in #394
• Stock plugins v2.0.0a2 by @timlrx in #395
• update version to v2.0.0a2 by @imda-peckyoke in #396
• update portal readme by @imda-amdlahir in #397
• Update SCA Workflow by @imda-benedictlee in #398
• Feature/view dataset list and upload by @imda-amdlahir in #389
• Fix: Return mock data for widget bundle in APIGW by @wanfei-imda in #411
• fixed bug in apigw for mockdata by @wanfei-imda in #416
• Update pre-build checks and codeql workflows for v2.x by @imda-kwokwk in #419
• Feature/Plugins Module by @wanfei-imda in #420
• Pydantic v2 by @timlrx in #436
• adding pytorch support by @iamksuresh in #372
• Fix/canvas by @imda-amdlahir in #437
• Feature/create project by @imda-amdlahir in #438
• temp rename tremur ui component folder by @imda-amdlahir in #440
• use test results flow by @imda-amdlahir in #441
• Update ale, fmtc, fmtr, image corruption widgets by @imda-amdlahir in #446
• Feature/inputs UI by @wanfei-imda in #452
• update canvas ux and add artifacts prop in grid item by @imda-amdlahir in #453
• update veritas dependencies by @timlrx in #451
• Input block data and multiple ux fixes and improvements in canvas by @imda-amdlahir in #454
• Algorithm (stock-plugin): Improve Image Corruption Toolbox User Experience by @jetkan-yk in #447
• Project Report Generation Flow & Template Module by @wanfei-imda in #455
• V2.x Smoke Test by @imda-benedictlee in #456
• Add Dockerfile for portal by @imda-peckyoke in #465
• Feature - test engine worker by @imda-peckyoke in #462
• Fix misc apigw issues by @imda-peckyoke in #466
• Fix/schema strict mode by @imda-peckyoke in #467
• Update docker compose file by @imda-peckyoke in #469
• Algorithm (stock-plugin): Enable PyTorch support to Image Robustness Toolkit + Refactor by @jetkan-yk in #463
• Fix errors in plugin upload with templates by @imda-peckyoke in #470
• Fi...

v2.0.0a2

AI Verify 2.0.0a2 Release Description
Date: 2025-02-03

We are excited to announce the release of AI Verify 2.0.0a2, the second alpha release of the AI Verify 2.x series. This release builds on the foundational improvements introduced in 2.0.0a1, delivering new features and enhancements that further empower users to validate and govern AI systems with greater flexibility, efficiency, and precision.

What’s New in AI Verify 2.0.0a2?

This release introduces significant updates to the toolkit, focusing on expanded functionality, improved usability, and enhanced integration capabilities. Here are the key highlights:

1. Veritas Plugin Integration

   • The addition of the Veritas Plugin enables financial institutions to meet common safety baseline and financial testing requirements, ensuring compliance with industry standards.

2. New Model Type: Uplift

   • A new model type, "uplift", has been introduced to support advanced use cases, particularly in scenarios requiring causal inference and uplift modeling.

3. Extended JSON Schema Limits

   • Increased name and description lengths in JSON schemas to accommodate more detailed and comprehensive metadata for projects, models, and datasets.

4. API Gateway Enhancements

   • New modules have been added to the API Gateway (API-GW), including:
     • Plugin Management
     • Test Model Configuration
     • Test Dataset Handling
     • Input Block Management
     • Project and Project Template Management
   • These modules streamline the integration and management of various components within the AI Verify ecosystem.

5. Input Block Meta Attribute: Group Number

   • A new meta attribute, groupNumber, has been added to input blocks, enabling better organization and grouping of inputs for complex workflows.

Building on AI Verify 2.0.0a1

This release continues the momentum from the first alpha version, which introduced:

• A modular architecture for improved performance and scalability.
• Independent test algorithms as standalone Python modules for greater flexibility.
• A refactored backend test engine as standalone python module.
• SQLite database integration for the API Gateway, ensuring lightweight and efficient data management.

What’s Next?

The AI Verify team is committed to delivering a robust and user-friendly toolkit for AI governance. Stay tuned for upcoming releases, which will include:

• New Frontend Workflows: A more intuitive and streamlined user interface for seamless navigation and task execution.
• Enhanced Computer Vision Support: Expanded evaluations to support a wider range of computer vision use cases.
• Additional Veritas Features: Further integration and capabilities, including the ability to generate Veritas reports and questionnaires, to meet evolving financial and safety testing requirements.

Get Started with AI Verify 2.0.0a2

We invite you to explore the new features and enhancements in this release. Your feedback is invaluable as we continue to refine and improve AI Verify. Together, let’s shape the future of AI governance and ensure the responsible deployment of AI systems.

AI Verify 2.0.0a2 – Empowering Trust in AI, One Step at a Time.

v2.0.0 alpha

Announcing AI Verify 2.x – A New Era of AI Governance!

We are thrilled to announce the upcoming release of AI Verify 2.x, a groundbreaking update that will revolutionize the way you validate and govern AI systems. This new version is packed with major enhancements designed to provide more flexibility, efficiency, and scalability.

What's New in AI Verify 2.0.0 Alpha?

• Modular Architecture: The toolkit has been re-architected to be more modular, allowing for easier integration and customization.
• Independent Test Algorithms: Each test algorithm can now be run as a separate Python module, giving you the freedom to use and develop algorithms independently.
• Redesigned Backend Test Engine Worker: The backend test engine worker has been completely redesigned for improved performance and reliability.

More to Come!

• New Frontend Workflows: Experience a more intuitive and streamlined user interface with our newly designed frontend workflows.
• Veritas Integration: Including Veritas into AI Verify allows financial institutions to meet common safety baseline and financial testing requirements.
• Improved support for Computer Vision: More evaluations to be included to support different types of use cases.

v0.10.3

What's Changed

• Updated the Testing Framework to incorporate controls in ISO/IEC 42001:2023 (#287)
  All controls in ISO/IEC 42001:2023 are mapped to the process checks in AI Verify testing framework. Organisations can use AI Verify toolkit to strengthen their AI governance, and practically demonstrate alignment with ISO/IEC 42001:2023, without onerous cost. Refer to crosswalk here (https://aiverifyfoundation.sg/resources/#crosswalk-with-iso42001)

• Added DevContainer by @berrydenhartog (#258)

• Improve Environment Variable Inheritance by @berrydenhartog (#267)

• Fix CI/CD for forked repo by @berrydenhartog (#282)

Minor Changes

• Omit Dev Dependencies during npm install (#285)
• Change IMDA-BTG references in GHA yml files to align with AI Verify Foundation (#289)

Full Changelog: v0.10.2...v0.10.3

v0.10.2

What's Changed

Updated Package Requirements, Setup Scripts and CI/CD Pipeline Workflows.

Bug Fixes

• Fixed issue in portal where error page is display upon report generation (#270)
• Fixed redis client reconnection issue (#266)
• Fixed image corruption plugin (#255)

Features for Improved Application Security

• Enhanced password security for db (#271)
• Enhanced file upload validation (#268)

Package Updates

• Apigw Packages (#270)
• Python Packages (#256)

Full Changelog:
v0.10.1...v0.10.2

v0.10.1

What's Changed

Bug fixes for the API Connector and implemented new application security features.

Bug Fixes:

• Fixed bug where SHAP and robustness test fails when model is connected via API.
• Fixed bug where test task updated hook does not execute while generating report.
• Updated node setup for Dockerfile, which was previously causing a 60s wait on build.

Features for improved application security:

• Removal of default usernames and passwords for Mongodb setup. Users are now required to define these credentials during setup.
• Upgraded npm packages (#231)
• Improved sanitization of file uploads (#231)
• Improved error messages and disabled stack trace to reduce information exposure (#242)
• Disabled autocompletion of sensitive fields (#231)
• CORS whitelist (#245) (#240)
• Updated the portal graphql client so that all browser requests use the portal rewrites for graphql client requests and subscriptions (#232)
• Other minor code cleanups to remove absolute file paths and default URLs (#243)

Full Changelog:
v0.10.0...v0.10.1

Installation Instructions

Build and run from Dockerfile
Build and run from Source Code

v0.10.0

What's Changed

New API Connector Feature!

We're excited to introduce a powerful enhancement to AI Verify – the API Connector feature!
Now, instead of uploading your AI Model file onto AI Verify, you can seamlessly configure an API Connection to your model server.

Learn more about the modes of accessing AI models here

Key Advantages:

• Bypass Size Limitations: Say goodbye to the constraints imposed by browser upload size limits.
• Test previously unsupported AI frameworks: The API Connector empowers you to work with models of any framework, such as PyTorch. Learn more about compatibility of model uploads here.
• Connection Settings for Batch Requests: Optimise your test runs by configuring connection timeouts, connection retries, max connections, rate limit and rate limit timeouts.

** Important Note: This feature currently supports tabular data only.

Getting Started:

To take advantage of this feature, refer to our How-To Guide. This guide provides step-by-step instructions on setting up the API Connector for seamless integration with your model server.

We are excited to have you try it out and hear what you think about this feature!
Discussion Board

Full Changelog: v0.9.4...v0.10.0

Installation Instructions

Build and run from Dockerfile
Build and run from Source Code