RRepoGEO

REPOGEO REPORT · LITE

harishsg993010/damn-vulnerable-MCP-server

Default branch main · commit 79734c19 · scanned 6/26/2026, 11:02:25 AM

GitHub: 1,313 stars · 161 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)

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

AI VISIBILITY SCORE
17 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 harishsg993010/damn-vulnerable-MCP-server, 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 to explicitly state LLM context focus

    Why:

    CURRENT
    # Damn Vulnerable Model Context Protocol (DVMCP)
A deliberately vulnerable implementation of the Model Context Protocol (MCP) for educational purposes.
    COPY-PASTE FIX
    # Damn Vulnerable Model Context Protocol (DVMCP)
A deliberately vulnerable implementation of the Model Context Protocol (MCP) for educational purposes, specifically targeting security vulnerabilities in Large Language Model (LLM) context provisioning.
  • hightopics#2
    Add relevant topics for AI/LLM security

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    llm-security, ai-security, vulnerable-by-design, model-context-protocol, mcp, security-training, cybersecurity-education, llm-vulnerabilities, ai-safety
  • highlicense#3
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with a standard open-source license (e.g., MIT, Apache-2.0, GPL-3.0) that reflects your intended usage and allows others to understand their rights and obligations.

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 harishsg993010/damn-vulnerable-MCP-server
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
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OWASP Top 10 for Large Language Model Applications · recommended 1×
  2. MITRE ATT&CK for Large Language Models (LLMs) · recommended 1×
  3. Hugging Face · recommended 1×
  4. Google Cloud · recommended 1×
  5. Microsoft Azure AI · recommended 1×
  • CATEGORY QUERY
    How can I learn about security vulnerabilities in LLM context provisioning protocols?
    you: not recommended
    AI recommended (in order):
    1. OWASP Top 10 for Large Language Model Applications
    2. MITRE ATT&CK for Large Language Models (LLMs)
    3. Hugging Face
    4. Google Cloud
    5. Microsoft Azure AI
    6. NCC Group

    AI recommended 6 alternatives but never named harishsg993010/damn-vulnerable-MCP-server. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Are there deliberately insecure AI context servers for practicing security assessments?
    you: not recommended
    AI recommended (in order):
    1. OWASP Top 10 for LLM Applications (OWASP/LLM-Security-Project)
    2. Lakera
    3. Giskard (Giskard-AI/giskard)
    4. Damn Vulnerable LLM
    5. Hugging Face Spaces
    6. Flask (pallets/flask)
    7. Django (django/django)
    8. OpenAI's GPT models
    9. Anthropic's Claude
    10. Hugging Face Inference API
    11. Hack The Box
    12. TryHackMe

    AI recommended 12 alternatives but never named harishsg993010/damn-vulnerable-MCP-server. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    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 harishsg993010/damn-vulnerable-MCP-server?
    pass
    AI did not name harishsg993010/damn-vulnerable-MCP-server — 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 harishsg993010/damn-vulnerable-MCP-server in production, what risks or prerequisites should they evaluate first?
    pass
    AI named harishsg993010/damn-vulnerable-MCP-server 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 harishsg993010/damn-vulnerable-MCP-server solve, and who is the primary audience?
    pass
    AI did not name harishsg993010/damn-vulnerable-MCP-server — 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?

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harishsg993010/damn-vulnerable-MCP-server — 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