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

FINDING #2423 · 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

SURPRISE0.0126

ENGAGEMENT0.41

FRESHNESS1.00

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

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

1% OF STARS IN ARCHIVE

Growth Telemetry

VELOCITY /D

5.00

ACCEL

+4.50

RETENTION

0.0%

PEAK 2026-08-28 · FORK-RETENTION 0.0% · 15 STARS / WINDOW

Author Audience

AUDIENCE

358

FOLLOWERS

OWNER ★

2,603

Engagement Signals

FORKS

126

ISSUE AUTH

PR AUTH

UNIQUE STARGAZERS 15 / 15 (DIVERSITY 1.00)

Why This Is A Finding

huhusmang/Awesome-LLMs-for-Vulnerability-Detection собрал 15 звёзд за окно, тогда как у автора всего 98 подписчиков — эффективная аудитория ≈ 358. Это даёт surprise-индекс 0.0126 (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 75% OF 9558 FINDINGS

#03wang2122/sprix-sage-router[ PYTHON ]0.80 #04zeenie-ai/OpenCompany[ PYTHON ]0.78 #20HRuiCcc/music-geshizhuanhuan[ PYTHON ]0.41 #22MerZlin/dsh-pet-indesktop[ PYTHON ]0.39 #24handsomejustin/easy_tdx[ PYTHON ]0.38

METRICS IN CONTEXT

MEDIAN ACROSS ALL 9558 FINDINGS · Δ vs MEDIAN · PERCENTILE = SHARE RANKED BELOW

METRICVALUEMEDIANΔ MEDPERCENTILE

SCORE0.010.00+0.00ABOVE 75%

VELOCITY5.005.67-0.67ABOVE 43%

RETENTION0.0%42.9%-42.9 PPABOVE 0%

FORKS126333-207ABOVE 32%

SURPRISE0.010.00+0.01ABOVE 67%