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

FINDING #3410 · UNIT ID 851138183

huhusmang/Awesome-LLMs-for-Vulnerability-Detection

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

[ PYTHON ][ GITHUB ↗ ]

SURPRISE SCORE

0.00

Score Breakdown

SURPRISE0.0086

ENGAGEMENT0.51

FRESHNESS1.07

SCORE = SURPRISE × ENGAGEMENT^0.7 × FRESHNESS × VISIBILITY × CONFIDENCE

SURPRISE = WINDOW STARS / DAYS / (AUDIENCE + FLOOR)

8% OF STARS IN ARCHIVE

Growth Telemetry

VELOCITY /D

3.43

ACCEL

+0.29

RETENTION

0.0%

PEAK 2026-08-29 · FORK-RETENTION 0.0% · 103 STARS / WINDOW

Author Audience

AUDIENCE

359

FOLLOWERS

OWNER ★

2,613

Engagement Signals

FORKS

130

ISSUE AUTH

PR AUTH

UNIQUE STARGAZERS 103 / 103 (DIVERSITY 1.00)

Why This Is A Finding

huhusmang/Awesome-LLMs-for-Vulnerability-Detection собрал 103 звёзд за окно, тогда как у автора всего 98 подписчиков — эффективная аудитория ≈ 359. Это даёт surprise-индекс 0.0086 (звёзды относительно охвата автора, а не в абсолюте). Удержание форков 0.0% и 0 внешних контрибьюторов отделяют реальный инструмент от разовой вспышки. Акселерация положительная — рост ещё не выдохся.

Related Findings

RANKS ABOVE 64% OF 9595 FINDINGS

#02guillaumemeyer/watermarks-remover[ PYTHON ]0.43 #09awesome-dsh-plugin/awesome-dsh-plugin[ PYTHON ]0.38 #10wang2122/sprix-sage-router[ PYTHON ]0.37 #16virgiliojr94/book-to-skill[ PYTHON ]0.31 #19ayghri/i-have-adhd[ PYTHON ]0.28

METRICS IN CONTEXT

MEDIAN ACROSS ALL 9595 FINDINGS · Δ vs MEDIAN · PERCENTILE = SHARE RANKED BELOW

METRICVALUEMEDIANΔ MEDPERCENTILE

SCORE0.010.00+0.00ABOVE 64%

VELOCITY3.433.67-0.23ABOVE 47%

RETENTION0.0%24.6%-24.6 PPABOVE 0%

FORKS130115+15ABOVE 53%

SURPRISE0.010.01+0.00ABOVE 51%