REPOGEO REPORT · LITE

jiep/offensive-ai-compilation

Default branch main · commit e489672e · scanned 6/18/2026, 12:47:38 PM

GitHub: 1,391 stars · 162 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

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 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

Reposition README opening to explicitly state it's a resource compilation, not a tool

Why:

CURRENT

A curated list of useful resources that cover Offensive AI.

COPY-PASTE FIX

This repository is a curated compilation of resources on Offensive AI, designed to provide a comprehensive overview of techniques, research, and defensive actions, rather than being a specific tool or framework. It serves as a foundational reference for understanding adversarial attacks, red teaming, and the exploitation of AI systems.

mediumtopics#2

Expand topics to include LLM and red teaming specifics

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, llm-security, ai-red-teaming, red-teaming

lowcomparison#3

Add a 'Comparison' section to the README

Why:

COPY-PASTE FIX

Add a new section to the README titled 'Comparison to Other Resources' that briefly explains how this compilation differs from specific tools (like ART, CleverHans) or frameworks (like MITRE ATLAS, OWASP LLM Top 10) by focusing on comprehensive resource aggregation rather than being an executable tool or a prescriptive standard.

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

Of all named tools, what % are you?

Top rival

Adversarial Robustness Toolbox (ART) by IBM

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Adversarial Robustness Toolbox (ART) by IBM · recommended 1×
OWASP Top 10 for Large Language Model Applications (LLM Top 10) · recommended 1×
Adversarial ML Threat Matrix (MITRE ATLAS) · recommended 1×
llm-garak/garak · recommended 1×
advbox/advbox · recommended 1×

CATEGORY QUERY
Where can I find resources for understanding adversarial attacks on machine learning systems?
you: not recommended
AI recommended (in order):
1. Adversarial Robustness Toolbox (ART) by IBM
AI recommended 1 alternative but never named jiep/offensive-ai-compilation. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are the best compilations of offensive AI security techniques and tools available?
you: not recommended
AI recommended (in order):
1. OWASP Top 10 for Large Language Model Applications (LLM Top 10)
2. Adversarial ML Threat Matrix (MITRE ATLAS)
3. Garak (llm-garak/garak)
4. AdvBox (advbox/advbox)
5. CleverHans (tensorflow/cleverhans)
6. TextAttack (TextAttack/TextAttack)
7. ART (Adversarial Robustness Toolbox) (Trusted-AI/adversarial-robustness-toolbox)
AI recommended 7 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 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?
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

Drop this badge into the README of jiep/offensive-ai-compilation. 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)

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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