REPOGEO REPORT · LITE

ottosulin/awesome-ai-security

Default branch main · commit 980abfa0 · scanned 6/22/2026, 5:17:42 AM

GitHub: 1,164 stars · 285 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 ottosulin/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

hightopics#1

Add relevant topics to the repository

Why:

COPY-PASTE FIX

ai-security, machine-learning-security, llm-security, generative-ai-security, adversarial-ml, ai-risk-management, ai-governance, red-teaming, awesome-list, cybersecurity, ai-safety, ml-security

mediumhomepage#2
Add a homepage URL to the repository
Why:
COPY-PASTE FIX
```
https://github.com/ottosulin/awesome-ai-security
```

lowreadme#3

Slightly refine the README's opening sentence for clarity

Why:

CURRENT

A curated list of awesome AI security related frameworks, standards, learning resources and open source tools.

COPY-PASTE FIX

This awesome list is a curated collection of frameworks, standards, learning resources, and open-source tools specifically focused on AI security, including machine learning and generative AI.

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 ottosulin/awesome-ai-security

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

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

OWASP Top 10 for Large Language Model Applications (LLM Top 10) · recommended 1×
NIST AI Risk Management Framework (AI RMF) · recommended 1×
Microsoft's Responsible AI Resources · recommended 1×
Google's AI Security Best Practices · recommended 1×
Adversarial Robustness Toolbox (ART) by IBM · recommended 1×

CATEGORY QUERY
Where can I find comprehensive resources for securing AI systems and machine learning models?
you: not recommended
AI recommended (in order):
1. OWASP Top 10 for Large Language Model Applications (LLM Top 10)
2. NIST AI Risk Management Framework (AI RMF)
3. Microsoft's Responsible AI Resources
4. Google's AI Security Best Practices
5. Adversarial Robustness Toolbox (ART) by IBM
6. MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems)
7. Hugging Face's Security Best Practices
AI recommended 7 alternatives but never named ottosulin/awesome-ai-security. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are the best frameworks and tools for AI red teaming and risk management?
you: not recommended
AI recommended (in order):
1. OWASP Top 10 for Large Language Models (LLMs)
2. Microsoft Counterfit (microsoft/counterfit)
3. IBM AI Fairness 360 (AIF360) (IBM/AIF360)
4. Google Responsible AI Toolkit
5. Adversarial Robustness Toolbox (ART) (Trusted-AI/adversarial-robustness-toolbox)
6. Giskard (Giskard-AI/giskard)
7. Fiddler AI Observability Platform
AI recommended 7 alternatives but never named ottosulin/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
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 ottosulin/awesome-ai-security?
pass
AI did not name ottosulin/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 ottosulin/awesome-ai-security in production, what risks or prerequisites should they evaluate first?
pass
AI named ottosulin/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 ottosulin/awesome-ai-security solve, and who is the primary audience?
pass
AI did not name ottosulin/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

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

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MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/ottosulin/awesome-ai-security.svg)](https://repogeo.com/en/r/ottosulin/awesome-ai-security)

HTML

<a href="https://repogeo.com/en/r/ottosulin/awesome-ai-security"><img src="https://repogeo.com/badge/ottosulin/awesome-ai-security.svg" alt="RepoGEO" /></a>

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ottosulin/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