RRepoGEO

REPOGEO REPORT · LITE

wuyoscar/ISC-Bench

Default branch main · commit cf984988 · scanned 6/2/2026, 5:21:54 AM

GitHub: 776 stars · 119 forks

AI VISIBILITY SCORE
40 /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
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 wuyoscar/ISC-Bench, 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's opening to highlight 'sensitive data generation'

    Why:

    CURRENT
    <h2 align="center">Internal Safety Collapse in Frontier Large Language Models</h2>
    COPY-PASTE FIX
    <h2 align="center">Internal Safety Collapse: Turning LLMs into Sensitive Data Generators</h2>
<p align="center">A benchmark for evaluating LLM and AI agent robustness against internal safety collapse and sensitive information leakage.</p>
  • mediumtopics#2
    Add more specific topics related to data leakage and privacy

    Why:

    CURRENT
    agent-safety, ai-safety, benchmark, jailbreak, large-language-models, llm-safety, red-teaming, safety-evaluation
    COPY-PASTE FIX
    agent-safety, ai-safety, benchmark, data-leakage, data-privacy, jailbreak, large-language-models, llm-safety, privacy-breach, red-teaming, safety-evaluation, sensitive-data
  • mediumlicense#3
    Clarify the existing license in the README

    Why:

    COPY-PASTE FIX
    ## License
This project is released under [describe your specific license(s) here, e.g., "a custom license combining elements of X and Y"]. Please refer to the [LICENSE file](LICENSE) for full details.

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 wuyoscar/ISC-Bench
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Giskard
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Giskard · recommended 1×
  2. MLCommons MLPerf Inference · recommended 1×
  3. OpenAI Evals · recommended 1×
  4. Anthropic's Constitutional AI · recommended 1×
  5. Hugging Face Evaluate Library · recommended 1×
  • CATEGORY QUERY
    How to benchmark large language models for potential safety vulnerabilities and data generation risks?
    you: not recommended
    AI recommended (in order):
    1. Giskard
    2. MLCommons MLPerf Inference
    3. OpenAI Evals
    4. Anthropic's Constitutional AI
    5. Hugging Face Evaluate Library
    6. Perspective API
    7. AdvBench
    8. Gauntlet

    AI recommended 8 alternatives but never named wuyoscar/ISC-Bench. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking tools to test AI agent robustness against internal safety collapse and sensitive information leakage.
    you: not recommended
    AI recommended (in order):
    1. Giskard (GiskardAI/giskard)
    2. Robust Intelligence (RI Platform)
    3. Adversarial Robustness Toolbox (ART) (Trusted-AI/adversarial-robustness-toolbox)
    4. DynoSafe
    5. OWASP Top 10 for LLMs
    6. Fiddler AI

    AI recommended 6 alternatives but never named wuyoscar/ISC-Bench. 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 wuyoscar/ISC-Bench?
    pass
    AI named wuyoscar/ISC-Bench explicitly

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

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

    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 wuyoscar/ISC-Bench. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/wuyoscar/ISC-Bench.svg)](https://repogeo.com/en/r/wuyoscar/ISC-Bench)
HTML
<a href="https://repogeo.com/en/r/wuyoscar/ISC-Bench"><img src="https://repogeo.com/badge/wuyoscar/ISC-Bench.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

wuyoscar/ISC-Bench — 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