RRepoGEO

REPOGEO REPORT · LITE

jiep/offensive-ai-compilation

Default branch main · commit 845a2d58 · scanned 5/8/2026, 8:27:44 PM

GitHub: 1,376 stars · 159 forks

AI VISIBILITY SCORE
33 /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
2 / 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 jiep/offensive-ai-compilation, 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
    Strengthen README's opening to clarify scope and audience

    Why:

    CURRENT
    # Offensive AI Compilation

A curated list of useful resources that cover Offensive AI.
    COPY-PASTE FIX
    # Offensive AI Compilation: A Comprehensive Awesome List for Offensive AI and Red Teaming

This is a comprehensive, curated awesome list of resources covering Offensive AI, designed for security researchers, red teamers, and anyone interested in understanding and mitigating AI security vulnerabilities.
  • mediumtopics#2
    Add 'awesome-list' and 'red-teaming' to topics

    Why:

    CURRENT
    adversarial-machine-learning, ai-security, artificial-intelligence, compilation, offensive-ai
    COPY-PASTE FIX
    adversarial-machine-learning, ai-security, artificial-intelligence, compilation, offensive-ai, awesome-list, red-teaming
  • lowreadme#3
    Add a brief comparison section to the README

    Why:

    COPY-PASTE FIX
    ## 🆚 Comparison with Other Resources 🆚

While resources like OWASP Top 10 for LLM and MITRE ATT&CK for ML provide valuable frameworks, this compilation offers a broader, curated collection of specific tools, research papers, and practical guides focused purely on offensive AI techniques and red teaming. It aims to complement these frameworks by providing actionable resources for implementation.

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 jiep/offensive-ai-compilation
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OWASP Top 10 for Large Language Model Applications (LLM Top 10)
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OWASP Top 10 for Large Language Model Applications (LLM Top 10) · recommended 1×
  2. MITRE ATT&CK for ML · recommended 1×
  3. Hugging Face's "Awesome MLOps" · recommended 1×
  4. Hugging Face's "Awesome AI Security" · recommended 1×
  5. Adversarial ML Threat Matrix (Microsoft) · recommended 1×
  • CATEGORY QUERY
    Where can I find a curated list of resources covering offensive AI and security vulnerabilities?
    you: not recommended
    AI recommended (in order):
    1. OWASP Top 10 for Large Language Model Applications (LLM Top 10)
    2. MITRE ATT&CK for ML
    3. Hugging Face's "Awesome MLOps"
    4. Hugging Face's "Awesome AI Security"
    5. Adversarial ML Threat Matrix (Microsoft)
    6. Google's AI Security Best Practices
    7. Papers With Code - Adversarial Attack Category
    8. Black Hat AI Village Archives
    9. DEF CON AI Village Archives

    AI recommended 9 alternatives but never named jiep/offensive-ai-compilation. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What resources are available for understanding and implementing adversarial machine learning attacks?
    you: not recommended
    AI recommended (in order):
    1. CleverHans
    2. Foolbox
    3. Adversarial Robustness Toolbox (ART) by IBM
    4. Awesome Adversarial Machine Learning GitHub Repository

    AI recommended 4 alternatives but never named jiep/offensive-ai-compilation. 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 jiep/offensive-ai-compilation?
    pass
    AI named jiep/offensive-ai-compilation explicitly

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

  • If a team adopts jiep/offensive-ai-compilation in production, what risks or prerequisites should they evaluate first?
    pass
    AI named jiep/offensive-ai-compilation 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 jiep/offensive-ai-compilation solve, and who is the primary audience?
    pass
    AI did not name jiep/offensive-ai-compilation — 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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jiep/offensive-ai-compilation — 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