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 requie · Agent Tool · ★ 135
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is LLMSecurityGuide safe to install? View the security audit →
🛡️ LLM Security 101: The Complete Guide (2026 Edition) A comprehensive guide to offensive and defensive security for Large Language Models and Agentic AI Systems, updated for February 2026 with the OWASP Top 10 for LLMs 2025, corrected OWASP Top 10 for Agentic Applications 2026 (ASI prefix), new security tools, recent incidents, and AI regulation coverage. Overview • What's New • Quick Start • OWASP LLM 2025 • 🆕 OWASP Agentic 2026 • Tools • Resources 🚨 BREAKING UPDATE - February 2026 ⚡ MAJOR UPDATE: This guide has been significantly updated with critical corrections and new content for 2026. The OWASP Agentic Top 10 identifiers have been corrected from the unofficial "AAI" prefix to the official "ASI" (Agentic Security Issue) prefix with proper
| Stars | 135 |
| Forks | 16 |
| Category | Agent Tool |
| Quality Score | 53.6021699883061/100 |
| Open Issues | 1 |
| Last Updated | 2026-04-03 |
| Created | 2025-10-07 |
| Est. Tokens | ~9k |
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LLMSecurityGuide is A comprehensive reference for securing Large Language Models (LLMs). Covers OWASP GenAI Top-10 risks, prompt injection, adversarial attacks, real-world incidents, and practical defenses. Includes cata. It is categorized as a Agent Tool with 135 GitHub stars.
You can find installation instructions and usage details in the LLMSecurityGuide GitHub repository at github.com/requie/LLMSecurityGuide. The project has 135 stars and 16 forks, indicating an active community.
The top alternatives to LLMSecurityGuide on Agent Skills Hub include agentseal, agent-audit, XSafeClaw. 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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