REPOGEO REPORT · LITE

huhusmang/Awesome-LLMs-for-Vulnerability-Detection

Default branch main · commit 5f80b18f · scanned 6/10/2026, 7:22:46 AM

GitHub: 906 stars · 76 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

22 /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

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 huhusmang/Awesome-LLMs-for-Vulnerability-Detection, 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 to clarify its nature as a research index

Why:

CURRENT

# Awesome Large Language Models for Vulnerability Detection 

| Title | Venue | Year | Paper | Github |

COPY-PASTE FIX

# Awesome Large Language Models for Vulnerability Detection 

This repository is the community's most comprehensive, continuously-updated index of research on Large Language Models for software vulnerability detection — covering papers across function-level, repository-level, agentic, and smart-contract detection, plus datasets, benchmarks, and surveys.

| Title | Venue | Year | Paper | Github |

highhomepage#2
Add a Homepage URL to the repository's About section
Why:
COPY-PASTE FIX
```
https://github.com/huhusmang/Awesome-LLMs-for-Vulnerability-Detection
```

mediumtopics#3

Refine existing topics to emphasize 'research index' nature

Why:

CURRENT

awesome-list, code-security, large-language-models, llm, security, static-analysis, vulnerability-detection

COPY-PASTE FIX

awesome-list, code-security, large-language-models, llm, security, static-analysis, vulnerability-detection, research-papers, literature-review, academic-research

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 huhusmang/Awesome-LLMs-for-Vulnerability-Detection

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Snyk Code

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Snyk Code · recommended 1×
github/codeql · recommended 1×
Checkmarx SAST · recommended 1×
Sonatype Nexus Lifecycle · recommended 1×
Veracode Static Analysis · recommended 1×

CATEGORY QUERY
How can I leverage AI to automatically find security flaws in my code?
you: not recommended
AI recommended (in order):
1. Snyk Code
2. GitHub Advanced Security (github/codeql)
3. Checkmarx SAST
4. Sonatype Nexus Lifecycle
5. Veracode Static Analysis
6. HCL AppScan Static Analyzer
AI recommended 6 alternatives but never named huhusmang/Awesome-LLMs-for-Vulnerability-Detection. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Where can I find comprehensive research on using LLMs for code vulnerability analysis?
you: not recommended
AI recommended (in order):
1. arXiv.org
2. Google Scholar
3. OWASP
4. Snyk
5. GitHub
6. Hugging Face
AI recommended 6 alternatives but never named huhusmang/Awesome-LLMs-for-Vulnerability-Detection. 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 huhusmang/Awesome-LLMs-for-Vulnerability-Detection?
pass
AI did not name huhusmang/Awesome-LLMs-for-Vulnerability-Detection — 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 huhusmang/Awesome-LLMs-for-Vulnerability-Detection in production, what risks or prerequisites should they evaluate first?
pass
AI named huhusmang/Awesome-LLMs-for-Vulnerability-Detection 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 huhusmang/Awesome-LLMs-for-Vulnerability-Detection solve, and who is the primary audience?
pass
AI did not name huhusmang/Awesome-LLMs-for-Vulnerability-Detection — 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

Drop this badge into the README of huhusmang/Awesome-LLMs-for-Vulnerability-Detection. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)

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HTML

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huhusmang/Awesome-LLMs-for-Vulnerability-Detection — 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