RRepoGEO

REPOGEO REPORT · LITE

OWASP/www-project-ai-testing-guide

Default branch main · commit 006e4e9e · scanned 6/13/2026, 9:02:11 PM

GitHub: 716 stars · 137 forks

AI VISIBILITY SCORE
15 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
0 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface OWASP/www-project-ai-testing-guide, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highabout#1
    Update the GitHub repository description

    Why:

    CURRENT
    OWASP Foundation web repository
    COPY-PASTE FIX
    Comprehensive guide for testing the security, reliability, and ethical alignment of AI systems, including LLMs and GenAI.
  • mediumhomepage#2
    Add the official project homepage URL

    Why:

    COPY-PASTE FIX
    https://[YOUR_OFFICIAL_OWASP_PROJECT_HOMEPAGE_URL]

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface OWASP/www-project-ai-testing-guide
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Privitar
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Privitar · recommended 1×
  2. Mostly AI · recommended 1×
  3. Synthesia · recommended 1×
  4. hashicorp/vault · recommended 1×
  5. AWS Key Management Service (KMS) · recommended 1×
  • CATEGORY QUERY
    What are the best practices for ensuring security and reliability in AI systems?
    you: not recommended
    AI recommended (in order):
    1. Privitar
    2. Mostly AI
    3. Synthesia
    4. HashiCorp Vault (hashicorp/vault)
    5. AWS Key Management Service (KMS)
    6. Azure Key Vault
    7. Google Cloud Key Management Service (KMS)
    8. Apache Kafka (apache/kafka)
    9. Apache Nifi (apache/nifi)
    10. Confluent Platform
    11. IBM Adversarial Robustness Toolbox (ART) (IBM/adversarial-robustness-toolbox)
    12. CleverHans (tensorflow/cleverhans)
    13. Valibot (fabian-hiller/valibot)
    14. Pydantic (pydantic/pydantic)
    15. Prometheus (prometheus/prometheus)
    16. Grafana (grafana/grafana)
    17. Splunk
    18. Elastic Stack (ELK)
    19. IBM AI Fairness 360 (AIF360) (IBM/AIF360)
    20. Google What-If Tool (tensorflow/tensorboard)
    21. Microsoft Fairlearn (fairlearn/fairlearn)
    22. LIME (Local Interpretable Model-agnostic Explanations) (marcotcr/lime)
    23. SHAP (SHapley Additive exPlanations) (shap/shap)
    24. Google Explainable AI
    25. Microsoft InterpretML (interpretml/interpret)
    26. MLflow (mlflow/mlflow)
    27. Weights & Biases
    28. Arize AI
    29. Datadog
    30. New Relic
    31. Datadog Logs
    32. PagerDuty
    33. Opsgenie
    34. Microsoft Threat Modeling Tool
    35. OWASP Threat Dragon (OWASP/threat-dragon)
    36. SonarQube (SonarSource/sonarqube)
    37. Snyk
    38. OWASP ZAP (zaproxy/zaproxy)
    39. Dependabot (github/dependabot)
    40. OneTrust
    41. TrustArc

    AI recommended 41 alternatives but never named OWASP/www-project-ai-testing-guide. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to approach ethical and security testing for large language models and AI agents?
    you: not recommended
    AI recommended (in order):
    1. Garak
    2. OWASP Top 10 for LLM Applications
    3. Microsoft Guidance for Responsible AI Development
    4. Azure AI Content Safety
    5. Azure AI Red Teaming
    6. IBM AI Fairness 360 (AIF360)
    7. Google's What-If Tool (WIT)
    8. Fairlearn
    9. Google's Differential Privacy Library
    10. OpenDP
    11. Adversarial Robustness Toolbox (ART)
    12. TextAttack
    13. Scale AI
    14. Anthropic's Constitutional AI

    AI recommended 14 alternatives but never named OWASP/www-project-ai-testing-guide. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of OWASP/www-project-ai-testing-guide?
    pass
    AI did not name OWASP/www-project-ai-testing-guide — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts OWASP/www-project-ai-testing-guide in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name OWASP/www-project-ai-testing-guide — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo OWASP/www-project-ai-testing-guide solve, and who is the primary audience?
    pass
    AI did not name OWASP/www-project-ai-testing-guide — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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OWASP/www-project-ai-testing-guide — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite