RRepoGEO

REPOGEO REPORT · LITE

microsoft/AI-Red-Teaming-Playground-Labs

Default branch main · commit 5453eac0 · scanned 5/25/2026, 11:26:45 PM

GitHub: 1,943 stars · 291 forks

AI VISIBILITY SCORE
22 /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
1 / 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 microsoft/AI-Red-Teaming-Playground-Labs, 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
  • highreadme#1
    Reposition README's opening paragraph to highlight LLM focus

    Why:

    CURRENT
    This repository contains the challenges for the labs used in the course "AI Red Teaming in Practice". The course was originally taught at Black Hat USA 2024 by Dr. Amanda Minnich and Gary Lopez. Martin Pouliot handled the infrastructure and scoring for the challenges. The challenges were designed by Dr. Amanda Minnich, Gary Lopez and Martin Pouliot. These challenges are available for anyone to use. The playground environment is based on Chat Copilot and was modified to be used in the course.
    COPY-PASTE FIX
    This repository provides a collection of hands-on challenges and labs for learning and practicing AI Red Teaming techniques, specifically focused on identifying and mitigating vulnerabilities in Large Language Models (LLMs) and generative AI systems. These labs are designed for security professionals and are based on the course "AI Red Teaming in Practice" taught at Black Hat USA 2024.
  • mediumtopics#2
    Expand topics to include LLM-specific terms

    Why:

    CURRENT
    ai-red-team, ai-red-teaming, prompt-injection
    COPY-PASTE FIX
    ai-red-team, ai-red-teaming, prompt-injection, llm-security, generative-ai-security, large-language-models
  • mediumhomepage#3
    Add a homepage URL

    Why:

    COPY-PASTE FIX
    Add a relevant URL (e.g., to the Microsoft Learn series or a related project page).

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 microsoft/AI-Red-Teaming-Playground-Labs
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Garak
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Garak · recommended 2×
  2. OWASP Top 10 for Large Language Model Applications (LLM Top 10) · recommended 1×
  3. LLM-Attacks · recommended 1×
  4. Red Teaming Language Models to Reduce Harms · recommended 1×
  5. Adversarial GLUE · recommended 1×
  • CATEGORY QUERY
    Looking for resources to learn and practice AI red teaming techniques for LLMs.
    you: not recommended
    AI recommended (in order):
    1. OWASP Top 10 for Large Language Model Applications (LLM Top 10)
    2. Garak
    3. LLM-Attacks
    4. Red Teaming Language Models to Reduce Harms
    5. Adversarial GLUE
    6. Prompt Engineering Guide
    7. Hugging Face Transformers

    AI recommended 7 alternatives but never named microsoft/AI-Red-Teaming-Playground-Labs. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to simulate and prevent prompt injection vulnerabilities in generative AI models?
    you: not recommended
    AI recommended (in order):
    1. Garak
    2. LLM Guard
    3. Prompt Security
    4. Rebuff
    5. OWASP LLM Top 10
    6. NeMo Guardrails
    7. LangChain

    AI recommended 7 alternatives but never named microsoft/AI-Red-Teaming-Playground-Labs. 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 microsoft/AI-Red-Teaming-Playground-Labs?
    pass
    AI did not name microsoft/AI-Red-Teaming-Playground-Labs — 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 microsoft/AI-Red-Teaming-Playground-Labs in production, what risks or prerequisites should they evaluate first?
    pass
    AI named microsoft/AI-Red-Teaming-Playground-Labs 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 microsoft/AI-Red-Teaming-Playground-Labs solve, and who is the primary audience?
    pass
    AI did not name microsoft/AI-Red-Teaming-Playground-Labs — 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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microsoft/AI-Red-Teaming-Playground-Labs — 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