RRepoGEO

REPOGEO REPORT · LITE

liudaizong/Awesome-LVLM-Attack

Default branch main · commit 119044b2 · scanned 6/6/2026, 5:27:55 AM

GitHub: 552 stars · 20 forks

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 liudaizong/Awesome-LVLM-Attack, 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
    awesome-list, lvlm-attack, vision-language-models, adversarial-attacks, ai-security, research-papers, survey
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Add a LICENSE file (e.g., MIT or Apache-2.0) to clarify usage terms.
  • mediumreadme#3
    Reposition the README's opening to clarify its 'awesome list' nature

    Why:

    CURRENT
    A **continual** collection of papers related to Attacks on Large-Vision-Language-Models (LVLMs).
    COPY-PASTE FIX
    A **continual** and **curated awesome list** of papers, methods, and resources related to Attacks on Large-Vision-Language-Models (LVLMs). This repository serves as a comprehensive reference for researchers and practitioners in AI security.

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 liudaizong/Awesome-LVLM-Attack
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 Models (LLMs)
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OWASP Top 10 for Large Language Models (LLMs) · recommended 1×
  2. OWASP AI Security and Privacy Guide · recommended 1×
  3. Microsoft Azure AI Red Team · recommended 1×
  4. microsoft/responsible-ai-toolbox · recommended 1×
  5. IBM Watson OpenScale · recommended 1×
  • CATEGORY QUERY
    How to assess and mitigate security risks in large multimodal AI systems?
    you: not recommended
    AI recommended (in order):
    1. OWASP Top 10 for Large Language Models (LLMs)
    2. OWASP AI Security and Privacy Guide
    3. Microsoft Azure AI Red Team
    4. Responsible AI Toolkit (microsoft/responsible-ai-toolbox)
    5. IBM Watson OpenScale
    6. Gretel.ai
    7. Robust Intelligence
    8. Snorkel AI
    9. Hugging Face Transformers (huggingface/transformers)
    10. Safetensors (huggingface/safetensors)

    AI recommended 10 alternatives but never named liudaizong/Awesome-LVLM-Attack. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the latest adversarial attack methods for vision-language models?
    you: not recommended
    AI recommended (in order):
    1. AdvCLIP
    2. Adversarial Patch for VLMs
    3. Universal Adversarial Perturbations (UAPs) for VLMs
    4. AutoPrompt for VLMs
    5. PGD/FGSM for VLMs

    AI recommended 5 alternatives but never named liudaizong/Awesome-LVLM-Attack. 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 liudaizong/Awesome-LVLM-Attack?
    pass
    AI did not name liudaizong/Awesome-LVLM-Attack — 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 liudaizong/Awesome-LVLM-Attack in production, what risks or prerequisites should they evaluate first?
    pass
    AI named liudaizong/Awesome-LVLM-Attack 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 liudaizong/Awesome-LVLM-Attack solve, and who is the primary audience?
    pass
    AI did not name liudaizong/Awesome-LVLM-Attack — 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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liudaizong/Awesome-LVLM-Attack — 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