菜单
AI trustworthiness testing using OWASP AI Testing Guide v1. Execute 32 test cases across 4 layers (Application, Model, Infrastructure, Data) with practical payloads and remediation.
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| name | owasp-ai-testing |
| description | AI trustworthiness testing using OWASP AI Testing Guide v1. Execute 32 test cases across 4 layers (Application, Model, Infrastructure, Data) with practical payloads and remediation. |
This skill enables AI agents to perform systematic trustworthiness testing of AI systems using the OWASP AI Testing Guide v1, published November 2025 by the OWASP Foundation.
The AI Testing Guide is the industry's first open standard for AI trustworthiness testing. Unlike vulnerability lists that identify WHAT risks exist, this guide provides a practical, repeatable methodology for HOW to test AI systems. It establishes 32 test cases across 4 layers, each with objectives, payloads, observable responses, and remediation guidance.
The guide's core principle: "Security is not sufficient, AI Trustworthiness is the real objective." AI systems fail for reasons beyond traditional security, including bias, hallucinations, misalignment, opacity, and data quality issues.
Use this skill to execute comprehensive AI testing, validate trustworthiness controls, prepare for audits, and build repeatable test suites for AI systems.
Combine with "OWASP LLM Top 10" for vulnerability identification, "NIST AI RMF" for risk management, or "ISO 42001 AI Governance" for governance compliance.
Invoke this skill when:
When executing this testing guide, gather:
Only run active tests on systems the user owns or is explicitly authorized to test. If testing_authorization is absent or ambiguous, limit the work to architecture review, checklist assessment, and safe hypothetical payload examples.
For production systems:
The OWASP AI Testing Guide organizes 32 test cases across four layers:
┌─────────────────────────────────────────┐
│ AI Application Layer │
│ (AITG-APP-01 to AITG-APP-14) │
│ Prompts, interfaces, outputs, agency │
├─────────────────────────────────────────┤
│ AI Model Layer │
│ (AITG-MOD-01 to AITG-MOD-07) │
│ Robustness, alignment, privacy │
├─────────────────────────────────────────┤
│ AI Infrastructure Layer │
│ (AITG-INF-01 to AITG-INF-06) │
│ Supply chain, resources, boundaries │
├─────────────────────────────────────────┤
│ AI Data Layer │
│ (AITG-DAT-01 to AITG-DAT-05) │
│ Training data, privacy, diversity │
└─────────────────────────────────────────┘
The guide defines 32 test cases across the four layers. Each case includes an objective,
payloads/procedure, observable responses, severity guidance, and remediation. The full catalog
— with payloads and a P0–P2 priority index — is in
references/test-cases.md. Read the layer section you are
testing rather than loading all 32 at once.
| Layer | Prefix | Cases | Focus |
|---|---|---|---|
| 1. Application | AITG-APP | 14 | Prompts, interfaces, outputs, agency |
| 2. Model | AITG-MOD | 7 | Robustness, alignment, privacy |
| 3. Infrastructure | AITG-INF | 6 | Supply chain, resources, boundaries |
| 4. Data | AITG-DAT | 5 | Training data, privacy, diversity |
Select cases by system type (see Step 1 of the procedure): LLM/chatbot → all APP + INF-01/02/03; ML classifier → all MOD + DAT-03/04; RAG → APP-02/03/08 + DAT-01/02; agent → APP-06 + INF-03/04; always include DAT-05 (privacy/consent).
Understand the system:
ai_system_description and system_architectureSelect test cases:
Prepare test environment:
Execute selected test cases layer by layer:
Application Layer (25-35 min)
Model Layer (15-20 min)
Infrastructure Layer (10-15 min)
Data Layer (10-20 min)
Score each finding:
| Severity | Description | Response Time |
|---|---|---|
| Critical | Exploitable vulnerability with high impact | Immediate |
| High | Significant risk, moderate exploitation difficulty | 7 days |
| Medium | Moderate risk, requires specific conditions | 30 days |
| Low | Minor risk, limited impact | 90 days |
| Info | Observation, no immediate risk | Backlog |
Compile findings into structured report.
Compile results into the OWASP AI Testing assessment report. The copy-ready template is in
references/report-template.md.
The report covers:
1.0 - Initial release (OWASP AI Testing Guide v1, November 2025)
Remember: AI trustworthiness testing goes beyond traditional security. A secure AI system that is biased, opaque, or unreliable is not trustworthy. Test comprehensively across all dimensions of trustworthiness.