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

FINDING #1180 · 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

SURPRISE0.0233

ENGAGEMENT0.59

FRESHNESS1.00

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

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

5% OF STARS IN ARCHIVE

Growth Telemetry

VELOCITY /D

9.29

ACCEL

+0.29

RETENTION

0.0%

PEAK 2026-08-29 · FORK-RETENTION 0.0% · 65 STARS / WINDOW

Author Audience

AUDIENCE

359

FOLLOWERS

OWNER ★

2,613

Engagement Signals

FORKS

130

ISSUE AUTH

PR AUTH

UNIQUE STARGAZERS 65 / 65 (DIVERSITY 1.00)

Why This Is A Finding

huhusmang/Awesome-LLMs-for-Vulnerability-Detection собрал 65 звёзд за окно, тогда как у автора всего 98 подписчиков — эффективная аудитория ≈ 359. Это даёт surprise-индекс 0.0233 (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 88% OF 9771 FINDINGS

#05MddIdd/mdd-sim-gateway[ PYTHON ]0.41 #08zeenie-ai/OpenCompany[ PYTHON ]0.38 #15litaolemo/xtquant_big_convert[ PYTHON ]0.33 #23917Dhj/DeepPaperNote[ PYTHON ]0.27 #24LBH-123-AI/Comfyui_Minimax_h3_latent_Upscaler[ PYTHON ]0.27

METRICS IN CONTEXT

MEDIAN ACROSS ALL 9771 FINDINGS · Δ vs MEDIAN · PERCENTILE = SHARE RANKED BELOW

METRICVALUEMEDIANΔ MEDPERCENTILE

SCORE0.020.00+0.01ABOVE 88%

VELOCITY9.293.71+5.57ABOVE 77%

RETENTION0.0%39.3%-39.3 PPABOVE 0%

FORKS13091+39ABOVE 59%

SURPRISE0.020.01+0.01ABOVE 73%