OWASP AI Testing Guide | OWASP Foundation (original) (raw)

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As organizations increasingly adopt artificial intelligence (AI) solutions, the need for a robust framework to rigorously test AI systems for security, ethics, reliability, and compliance becomes essential. Although numerous application security testing guides exist, such as the OWASP Web Security Testing Guide (WSTG) and Mobile Security Testing Guide (MSTG), the unique risks and challenges of AI systems require specialized guidance.

The OWASP AI Testing Guide aims to become the reference for identifying security, privacy, ethical, and compliance vulnerabilities inherent in AI applications. Inspired by established OWASP methodologies, the AI Testing Guide will deliver structured and practical guidance for security professionals, testers, and AI developers. This guide will be technology and industry agnostic, emphasizing applicability across various AI implementation scenarios.

Importance of AI Testing

AI testing is vital because AI now underpins critical decision-making and daily operations across industries, from healthcare and finance to automotive and cybersecurity. To ensure an AI system is truly reliable, secure, accurate, and ethical, testing must go well beyond basic functionality. It needs to validate bias and fairness controls to prevent discrimination, conduct adversarial robustness checks against crafted inputs designed to fool or hijack models, and perform security and privacy assessments, such as model-extraction, data-leakage, and poisoning attack simulations. Incorporating techniques like differential privacy ensures compliance with data-protection laws while safeguarding individual records. This guide’s comprehensive approach to AI testing aims to uncover hidden risks and maintain trust in AI-driven solutions.

Why AI Testing is Unique

Testing AI systems presents unique challenges compared to traditional software testing. AI models, especially those based on machine learning (ML), exhibit non-deterministic behavior; since many AI algorithms involve randomness, during training or inference making outputs probabilistic rather than repeatable. This demands specialized regression and stability tests that account for acceptable variance. AI models learn from and depend on the quality and distribution of their training data. Unlike traditional code, changing inputs (data drift) can silently degrade performance, so tests must validate both data quality and model output over time.

Because AI systems depend so heavily on the quality and integrity of their training and input data, data-centric testing methodologies become indispensable to ensure reliable, fair, and accurate model performance. Unintended biases hidden in training data can lead to discriminatory outcomes. AI testing must include fairness assessments and mitigation strategies, which are not part of conventional software QA. The complexity and “black-box” nature of certain AI systems, such as deep learning neural networks, can obscure internal decision-making processes, complicating verification and validation. Adversarial or offensive security testing isn’t just recommended, it’s indispensable for AI technologies.

Because AI models can be fooled or manipulated by carefully crafted inputs (adversarial examples), organizations must employ dedicated adversarial robustness testing methodologies that extend well beyond standard functional tests. Without these specialized security assessments, AI systems remain vulnerable to subtle attacks that can compromise their integrity, reliability, and overall trustworthiness.

Finally all AI models live in ever-changing environments hence ongoing monitoring and automated re-validation of both data and model performance are essential to catch drift, emerging biases, or new vulnerabilities.

Purpose and Scope of the OWASP AI Testing Guide

The OWASP AI Testing Guide is designed to serve as a comprehensive reference for software developers, architects, data analysts, researchers, and risk officers, ensuring that AI risks are systematically addressed throughout the product development lifecycle. It outlines a robust suite of tests, ranging from data‐centric validation and fairness assessments to adversarial robustness and continuous performance monitoring, that collectively provide documented evidence of risk validation and control. By following this guidance, teams can establish the level of trust required to confidently deploy AI systems into production, with verifiable assurances that potential biases, vulnerabilities, and performance degradations have been proactively identified and mitigated.

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Road Map

Road Map

We wrote a first draft here

Phase 1: Initial Draft and Community Formation (JUNE 2025) Publish an initial project outline clearly defining the scope, mission, and testing categories. Establish an OWASP GitHub repository and set up a dedicated community team. Conduct initial outreach to invite contributions from the OWASP and AI communities.

Phase 2: Framework Development and First Release (SEPTEMBER 2025) Develop detailed testing guidelines covering key AI-specific risks including model security, data poisoning, adversarial robustness, prompt injection, privacy, and ethics validation. Publish a draft version for public review and community feedback. Begin pilot testing the guide’s methodologies in collaboration with industry partners to gather practical insights and validate effectiveness.

Phase 3: Refinement, Release, and Promotion (DECEMBER 2025) Incorporate community and industry feedback to finalize the first official release of the OWASP AI Testing Guide. Present the guide at global OWASP conferences, including hosting workshops and interactive sessions to encourage broader adoption and continuous improvement. Establish a structured update cycle to ensure ongoing relevance with advancements in AI.

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