RRepoGEO

REPOGEO REPORT · LITE

mrwadams/attackgen

Default branch main · commit 85238ac5 · scanned 6/30/2026, 3:16:42 PM

GitHub: 1,222 stars · 163 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
35 /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
3 / 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 mrwadams/attackgen, 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
    cybersecurity, incident-response, mitre-attack, llm, ai, scenario-generation, security-testing, red-team, blue-team, threat-intelligence
  • highreadme#2
    Clarify the README's opening to emphasize open-source scenario generation

    Why:

    CURRENT
    # AttackGen

AttackGen is a cybersecurity incident response testing tool that leverages the power of large language models and the comprehensive MITRE ATT&CK and ATLAS frameworks. The tool generates tailored incident response scenarios based on user-selected threat actor groups, AI attack case studies, and your organisation's details.
    COPY-PASTE FIX
    # AttackGen: Open-Source AI for Incident Response Scenario Generation

AttackGen is an open-source cybersecurity incident response scenario generation tool that leverages large language models and the comprehensive MITRE ATT&CK and ATLAS frameworks. It generates tailored incident response scenarios based on user-selected threat actor groups, AI attack case studies, and your organization's details.
  • mediumhomepage#3
    Add a homepage URL to the repository

    Why:

    COPY-PASTE FIX
    https://github.com/mrwadams/attackgen

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 mrwadams/attackgen
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
AttackIQ
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. AttackIQ · recommended 2×
  2. Picus Security · recommended 2×
  3. Mandiant Advantage · recommended 1×
  4. Palo Alto Networks Cortex XBC · recommended 1×
  5. Splunk Security Orchestration, Automation, and Response (SOAR) · recommended 1×
  • CATEGORY QUERY
    Need a tool to generate tailored cybersecurity incident response scenarios for testing.
    you: not recommended
    AI recommended (in order):
    1. AttackIQ
    2. Picus Security
    3. Mandiant Advantage
    4. Palo Alto Networks Cortex XBC
    5. Splunk Security Orchestration, Automation, and Response (SOAR)
    6. Atomic Red Team

    AI recommended 6 alternatives but never named mrwadams/attackgen. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best platforms for AI-driven cyber attack simulation and incident training?
    you: not recommended
    AI recommended (in order):
    1. AttackIQ
    2. Mandiant Advantage Security Validation
    3. Cymulate
    4. Picus Security
    5. SafeBreach
    6. RangeForce
    7. Cyberbit Range

    AI recommended 7 alternatives but never named mrwadams/attackgen. 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 mrwadams/attackgen?
    pass
    AI named mrwadams/attackgen explicitly

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

  • If a team adopts mrwadams/attackgen in production, what risks or prerequisites should they evaluate first?
    pass
    AI named mrwadams/attackgen 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 mrwadams/attackgen solve, and who is the primary audience?
    pass
    AI named mrwadams/attackgen explicitly

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

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MARKDOWN (README)
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mrwadams/attackgen — 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