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1.2 Principles of OWASP AI Testing

Trustworthy AI is achieved through the combined strength of three foundational domains — Responsible AI (RespAI), Security AI (SecAI), and Privacy AI (PrivacyAI). These domains form the testable foundation of Trustworthy AI within the OWASP AI Testing Framework. While broader definitions of Trustworthy AI may also encompass governance, reliability, and accountability, these qualities are enabled and operationalized through continuous testing across the three domains below.

Effective AI testing integrates these dimensions holistically:

• Security ensures resilience against adversarial and infrastructural threats.
• Privacy protects confidentiality and prevents misuse or inference of sensitive data.
• Responsible AI enforces ethical, transparent, and bias-resistant behavior.

Together, they form a unified structure for validating, controlling, and sustaining Trustworthy AI Systems : systems that operate safely, predictably, and in alignment with human values.

1. Security (SecAI)

AI systems must be resilient to adversarial threats and systemic exploitation, ensuring protection across the full AI stack and lifecycle.

• Prompt & Input Control: Safeguard system prompts, instructions, and user inputs from injection or manipulation.
• Adversarial Robustness: Test resistance to evasion, poisoning, model theft, jailbreaks, and indirect prompt injections.
• Infrastructure Security: Assess API endpoints, plugins, RAG pipelines, and agentic workflows for vulnerabilities.
• Supply-Chain Risk: Inspect models and dependencies for poisoning, tampering, or third-party compromise.
• Continuous Testing: Integrate automated adversarial and dependency scanning into CI/CD pipelines.

2. Privacy (PrivacyAI)

Ensure confidentiality and user control over data exposed to or generated by AI systems throughout the model lifecycle.

• Data Leakage Prevention: Detect unintended disclosures of training data, private context, or user inputs.
• Membership & Property Inference Resistance: Evaluate susceptibility to attacks that infer if data was part of training.
• Model Extraction & Exfiltration: Simulate adversaries attempting to replicate proprietary models or weights.
• Data-Governance Compliance: Validate adherence to principles of minimization, purpose limitation, and consent management.

3. Responsible AI (RespAI)

Promote ethical, safe, and aligned system behavior through ongoing evaluation and mitigation.

• Bias & Fairness Audits: Identify discriminatory outputs across demographic groups and edge cases.
• Toxicity & Abuse Detection: Test resilience against producing or amplifying harmful or misleading content.
• Safety Alignment: Validate adherence to alignment constraints and resistance to jailbreak or role-play exploits.
• Guardrail Coverage: Evaluate safety filters, refusal mechanisms, and abuse-prevention logic.
• Human-in-the-Loop Controls: Ensure escalation and review pathways for high-impact decisions.

4. Trustworthy AI Systems

Trustworthy AI = RespAI + SecAI + PrivacyAI, supported by governance, transparency, and monitoring mechanisms that preserve trust over time.

• Explainability: Ensure users and auditors can interpret how and why decisions are made.
• Consistency & Stability: Verify predictable responses under prompt variations and regression tests.
• Continuous Monitoring: Apply runtime observability, drift detection, and automated anomaly alerting.
• Lifecycle Testing: Extend validation from design to deployment and post-market phases.
• Policy & Regulatory Alignment: Map testing and validation processes to frameworks such as NIST AI RMF [1], ISO/IEC 42001 [2], and the OWASP Top 10 for LLMs [3].

Effective AI testing is built upon three macro domains: Security, Privacy, Responsible AI,to build Trustworthy AI Systems. We chose these 3 core domains because they collectively address the full range of AI risks. Security ensures resilience against adversarial and infrastructure threats. Privacy prevents unintended data exposure and inference attacks. Responsible AI focuses on ethical behavior and fairness, guarding against bias and misuse. Together, they form a comprehensive framework for validating, controlling, and sustaining safe and reliable AI deployments. Each domain includes key principles that guide the evaluation of modern AI applications.

When to Test AI

ISO/IEC 23053 [4] structures the ML-based AI system lifecycle into a series of repeatable phases, each with clear objectives, artifacts, and governance touchpoints:

1. Planning & Scoping: In this phase, you establish clear business objectives, success metrics, and ML use cases while identifying key stakeholders, regulatory requirements, and the organization’s risk tolerance.
2. Data Preparation: In this phase, you gather and document raw data sources, conduct profiling and quality checks through preprocessing pipelines, and implement versioning and lineage tracking for full data traceability.
3. Model Development & Training: In this phase, you choose appropriate algorithms and architectures, train models on curated datasets with feature engineering, and record experiments, including the parameters that govern the learning process (i.e. hyperparameters) and performance metrics in a model registry.
4. Validation & Evaluation: in this phase, you test models using reserved and adversarial datasets, perform fairness, robustness, and security evaluations, and ensure they meet functional, ethical, and regulatory standards.
5. Deployment & Integration: in this phase, you are preparing and bundling your trained AI model into a deployable artifact for either service (i.e. wrap the model in a microservice or API) or edge deployment (i.e. convert and optimize the model for resource-constrained devices such as IoT gateways or mobile phones) automate build-test-release workflows via CI/CD, and verify infrastructure security measures
6. Operation & Maintenance: in this phase while the AI product is in production environment, you will continuously monitor performance, data drift, and audit logs, triggering alerts on anomalies or compliance breaches, while periodically retraining models with fresh data, re-validating security, privacy, and fairness controls, and updating documentation, training, and policies as needed.

AI testing should be integrated throughout the entire AI system lifecycle to ensure AI systems remain accurate, secure, fair, and trustworthy from inception through ongoing operation:

1. Planning & Scoping Phase: Confirm that business objectives, success metrics, and ML use cases are testable and traceable. Identify AI-specific risks (adversarial, privacy, compliance) and map them to controls. Verify stakeholder roles, regulatory constraints, and risk-tolerance criteria are documented.
2. Data Preparation: Perform data quality tests to check for missing values, outliers, schema mismatches, and duplicates.Validate feature distributions (i.e. how the values of a particular variable are spread out or arranged) against historical profiles to set drift thresholds (i.e. for data drifts from this baseline). Ensure every data source, transformation, and version is recorded and traceable.
3. Model Development & Training: Validate preprocessing code, custom layers, and feature engineering functions behave as expected. Run static code scans (e.g. SAST) on model code for insecure dependencies or misconfigurations. Confirm no data leakage between training, validation, and test splits. Ensure tuning changes improve generalization without regressions.
4. Validation & Evaluation: Validate performance against benchmarks to measure accuracy, precision/recall, AUC, etc., on hold-out and adversarial test sets. Conduct fairness & bias audits to evaluate model outputs across demographic slices and edge cases. Conduct adversarial robustness tests by applying well-known techniques for crafting adversarial examples against neural networks or other adversarial attacks to assess resistance. Conduct privacy attacks to simulate membership inference, model extraction, and poisoning to confirm privacy protections. Verify model decisions are interpretable and valid by attributing predictions back to input features.
5. Operation & Maintenance: Conduct regression tests for drift detection by Continuously comparing production inputs and outputs to validation baselines. Verify monitoring rules fire correctly on performance dips, data drift, or security anomalies.Re-evaluate performance, fairness, and robustness after model updates or data refreshes. Periodically confirm that security, privacy, and ethical controls remain effective and documented.

Among the testing goals of this guide is to integrate OWASP’s LLM‐specific test cases and broader OWASP AI Exchange [5] threats into your lifecycle phases to ensure both pre‐release validation and continuous protection against emerging vulnerabilities. During planning and scoping phase for example threat modeling exercises can be used to enumerate OWASP Top 10 LLM risks (prompt injection, data leakage, model poisoning, over‐reliance, etc.) and AI Exchange threats to define your test scope and controls.

During the Validation & Evaluation phase for example, prompt injection tests can test direct and indirect prompt manipulations to verify guardrail coverage and refusal behaviors and Inject malicious samples in a controlled retraining loop to ensure poisoning defenses work, During development & operation tests can be directed to continuously scan newly installed or updated plugins for OWASP‐identified weaknesses and to monitor outputs for signs of jailbreaks, back‐door prompts, or exploitation of known OWASP AI Exchange threat vectors.

In this initial release, the OWASP AI testing methodology is focused on guiding AI product owners to define the test scope and execute a comprehensive suite of assessments once an initial AI product version is test-ready; future updates will expand this guidance to cover earlier pre-production phases as well.