RRepoGEO

REPOGEO REPORT · LITE

NVIDIA/garak

Default branch main · commit fd252e55 · scanned 5/28/2026, 6:31:23 AM

GitHub: 7,953 stars · 975 forks

AI VISIBILITY SCORE
92 /100
Healthy
Category recall
2 / 2
Avg rank #1.0 when recommended
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 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 NVIDIA/garak, 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

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

OVERALL DIRECTION
  • highhomepage#1
    Update the repository's Homepage field to the official project website

    Why:

    CURRENT
    https://discord.gg/uVch4puUCs
    COPY-PASTE FIX
    https://garak.ai
  • mediumreadme#2
    Ensure key project URLs are hyperlinked in the README

    Why:

    CURRENT
    ### > See our user guide! docs.garak.ai
### > Project links & home: garak.ai
    COPY-PASTE FIX
    ### > See our user guide! [docs.garak.ai](https://docs.garak.ai)
### > Project links & home: [garak.ai](https://garak.ai)
  • lowtopics#3
    Add 'llm-red-teaming' to the repository topics

    Why:

    CURRENT
    ai, llm-evaluation, llm-security, security-scanners, vulnerability-assessment
    COPY-PASTE FIX
    ai, llm-evaluation, llm-security, security-scanners, vulnerability-assessment, llm-red-teaming

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
2 / 2
100% of queries surface NVIDIA/garak
Avg rank
#1.0
Lower is better. #1 = top recommendation.
Share of voice
13%
Of all named tools, what % are you?
Top rival
laiyer-ai/llm-guard
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. laiyer-ai/llm-guard · recommended 1×
  2. NVIDIA/NeMo-Guardrails · recommended 1×
  3. langchain-ai/langchain · recommended 1×
  4. openai/evals · recommended 1×
  5. Adversarial Robustness Toolbox (ART) · recommended 1×
  • CATEGORY QUERY
    How can I test my large language model for security vulnerabilities like prompt injection?
    you: #1
    AI recommended (in order):
    1. Garak (Garak-LLM/Garak) ← you
    2. LLM Guard (laiyer-ai/llm-guard)
    3. NeMo Guardrails (NVIDIA/NeMo-Guardrails)
    4. LangChain (langchain-ai/langchain)
    5. OpenAI Evals (openai/evals)
    Show full AI answer
  • CATEGORY QUERY
    What tools help red team generative AI models to detect hallucination and data leakage?
    you: #1
    AI recommended (in order):
    1. Garak ← you
    2. Adversarial Robustness Toolbox (ART)
    3. Microsoft Counterfit
    4. OWASP LLM Top 10 Project
    5. LangChain
    6. LlamaIndex
    7. Fiddler AI
    8. transformers
    9. openai
    10. nltk
    11. spacy
    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 NVIDIA/garak?
    pass
    AI named NVIDIA/garak explicitly

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

  • If a team adopts NVIDIA/garak in production, what risks or prerequisites should they evaluate first?
    pass
    AI named NVIDIA/garak explicitly

    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 NVIDIA/garak solve, and who is the primary audience?
    pass
    AI named NVIDIA/garak explicitly

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

Embed your GEO score

Drop this badge into the README of NVIDIA/garak. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/NVIDIA/garak.svg)](https://repogeo.com/en/r/NVIDIA/garak)
HTML
<a href="https://repogeo.com/en/r/NVIDIA/garak"><img src="https://repogeo.com/badge/NVIDIA/garak.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

NVIDIA/garak — 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