RRepoGEO

REPOGEO REPORT · LITE

TalEliyahu/Awesome-AI-Security

Default branch main · commit 2ac3889c · scanned 6/9/2026, 2:48:22 PM

GitHub: 724 stars · 153 forks

AI VISIBILITY SCORE
27 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 TalEliyahu/Awesome-AI-Security, 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 the README H1 to explicitly state it's a curated collection

    Why:

    CURRENT
    # Awesome AI Security
Curated resources, research, and tools for securing AI systems.
    COPY-PASTE FIX
    # Awesome AI Security: A Curated Collection of Resources, Research, and Tools for Securing AI Systems
  • hightopics#2
    Add 'awesome-list' and more specific AI security topics

    Why:

    CURRENT
    artificial-intelligence, cybersecurity
    COPY-PASTE FIX
    artificial-intelligence, cybersecurity, awesome-list, ai-security, machine-learning-security, adversarial-ai, ai-governance, ai-risk-management
  • mediumreadme#3
    Add a concise 'What is this repository?' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section immediately after the initial description and before the Table of Contents, e.g., `## What is this repository?
This repository serves as a comprehensive, curated hub for all things related to AI security. It is not a tool or a framework itself, but rather a guide to the best practices, research, tools, and datasets available to secure AI systems.`

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 TalEliyahu/Awesome-AI-Security
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
IBM/adversarial-robustness-toolbox
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. IBM/adversarial-robustness-toolbox · recommended 1×
  2. tensorflow/cleverhans · recommended 1×
  3. Azure/counterfit · recommended 1×
  4. tensorflow/privacy · recommended 1×
  5. openai/robustness · recommended 1×
  • CATEGORY QUERY
    How can I find resources for securing machine learning models against adversarial attacks?
    you: not recommended
    AI recommended (in order):
    1. Adversarial Robustness Toolbox (ART) (IBM/adversarial-robustness-toolbox)
    2. CleverHans (tensorflow/cleverhans)
    3. Microsoft Counterfit (Azure/counterfit)
    4. TensorFlow Privacy (tensorflow/privacy)
    5. OpenAI's 'Robustness' GitHub repository (openai/robustness)
    6. MITRE ATLAS

    AI recommended 6 alternatives but never named TalEliyahu/Awesome-AI-Security. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best practices and frameworks for AI system cybersecurity governance?
    you: not recommended
    AI recommended (in order):
    1. NIST AI Risk Management Framework (AI RMF)
    2. NIST Cybersecurity Framework (CSF)
    3. ISO/IEC 27001 (Information Security Management Systems)
    4. OWASP Top 10 for Large Language Models (LLMs)
    5. MITRE ATT&CK for Machine Learning (ML)
    6. CIS Critical Security Controls (CIS Controls)
    7. GDPR (General Data Protection Regulation)
    8. CCPA (California Consumer Privacy Act)

    AI recommended 8 alternatives but never named TalEliyahu/Awesome-AI-Security. This is the gap to close.

    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 TalEliyahu/Awesome-AI-Security?
    pass
    AI did not name TalEliyahu/Awesome-AI-Security — 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 TalEliyahu/Awesome-AI-Security in production, what risks or prerequisites should they evaluate first?
    pass
    AI named TalEliyahu/Awesome-AI-Security 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 TalEliyahu/Awesome-AI-Security solve, and who is the primary audience?
    pass
    AI did not name TalEliyahu/Awesome-AI-Security — 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?

Embed your GEO score

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TalEliyahu/Awesome-AI-Security — 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