No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by MorDavid · LLM Plugin · ★ 1.7k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is BruteForceAI safe to install? View the security audit →
🤖 BruteForceAI - AI-Powered Login Brute Force Tool Advanced LLM-powered brute-force tool combining AI intelligence with automated login attacks Features • Installation • Usage • Examples • Configuration • License 🎯 About BruteForceAI is an advanced penetration testing tool that revolutionizes traditional brute-force attacks by integrating Large Language Models (LLM) for intelligent form analysis. The tool automatically identifies login form selectors using AI, then executes sophisticated multi-threaded attacks with human-like behavior patterns. 🧠 LLM-Powered Form Analysis Stage 1 (AI Analysis): LLM analyzes HTML content to identify login form elements and selectors Stage 2 (Smart Attack): Executes intelligent brute-force attacks using AI-discovered selectors 🚀 Advanced Attack Features Multi-threaded execution with sync
| Stars | 1,714 |
| Forks | 337 |
| Language | Python |
| Category | LLM Plugin |
| Quality Score | 71.899908998408/100 |
| Open Issues | 1 |
| Last Updated | 2026-07-17 |
| Created | 2025-07-19 |
| Platforms | python |
| Est. Tokens | ~16k |
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BruteForceAI is Advanced LLM-powered brute-force tool combining AI intelligence with automated login attacks. It is categorized as a LLM Plugin with 1.7k GitHub stars.
BruteForceAI is primarily written in Python. It covers topics such as ai, bruteforce, bugbounty.
You can find installation instructions and usage details in the BruteForceAI GitHub repository at github.com/MorDavid/BruteForceAI. The project has 1.7k stars and 337 forks, indicating an active community.
The top alternatives to BruteForceAI on Agent Skills Hub include awesome-hacking-lists, lemonade, klavis. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
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