RRepoGEO

REPOGEO REPORT · LITE

thunlp/OpenAttack

Default branch master · commit 4df712e0 · scanned 6/3/2026, 8:41:37 PM

GitHub: 776 stars · 127 forks

AI VISIBILITY SCORE
90 /100
Healthy
Category recall
2 / 2
Avg rank #2.0 when recommended
Rule findings
2 pass · 0 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 thunlp/OpenAttack, 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 the README's opening statement to emphasize textual adversarial attacks

    Why:

    CURRENT
    OpenAttack is an open-source Python-based textual adversarial attack toolkit, which handles the whole process of textual adversarial attacking, including preprocessing text, accessing the victim model, generating adversarial examples and evaluation.
    COPY-PASTE FIX
    OpenAttack is a comprehensive, open-source Python toolkit specifically designed for **textual adversarial attacks**, providing a unified framework to handle the entire process from preprocessing text and accessing victim models to generating adversarial examples and evaluating robustness.
  • mediumreadme#2
    Add a dedicated section to the README highlighting OpenAttack's unique value

    Why:

    COPY-PASTE FIX
    Add a new section, such as "## Why OpenAttack?" or "## OpenAttack's Differentiators", explaining its comprehensive, unified framework for textual attacks and broad support for algorithms and metrics under a consistent API.
  • lowtopics#3
    Add `nlp-robustness` to the repository topics

    Why:

    CURRENT
    adversarial-attacks, adversarial-example, natural-language-processing, nlp, pytorch
    COPY-PASTE FIX
    adversarial-attacks, adversarial-example, natural-language-processing, nlp, pytorch, nlp-robustness

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
2 / 2
100% of queries surface thunlp/OpenAttack
Avg rank
#2.0
Lower is better. #1 = top recommendation.
Share of voice
18%
Of all named tools, what % are you?
Top rival
TextAttack/TextAttack
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. TextAttack/TextAttack · recommended 1×
  2. IBM/adversarial-robustness-toolbox · recommended 1×
  3. LinyuanLyu/DeepRobust · recommended 1×
  4. huggingface/transformers · recommended 1×
  5. bethgelab/foolbox · recommended 1×
  • CATEGORY QUERY
    How can I generate adversarial examples to test the robustness of NLP models?
    you: #2
    AI recommended (in order):
    1. TextAttack (TextAttack/TextAttack)
    2. OpenAttack (THUDM/OpenAttack) ← you
    3. Adversarial Robustness Toolbox (ART) (IBM/adversarial-robustness-toolbox)
    4. DeepRobust (LinyuanLyu/DeepRobust)
    5. Hugging Face Transformers (huggingface/transformers)
    6. Foolbox (bethgelab/foolbox)
    Show full AI answer
  • CATEGORY QUERY
    What Python tools exist for evaluating NLP model robustness against textual perturbations?
    you: #2
    AI recommended (in order):
    1. TextAttack
    2. OpenAttack ← you
    3. ART
    4. Hugging Face Evaluate
    5. NL-Augmenter
    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 thunlp/OpenAttack?
    pass
    AI named thunlp/OpenAttack explicitly

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

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

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

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thunlp/OpenAttack — 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