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 seojoonkim · Codex Skill · ★ 172
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is prompt-guard safe to install? View the security audit →
🛡️ Prompt Guard Prompt injection defense for any LLM agent Protect your AI agent from manipulation attacks. Works with Clawdbot, LangChain, AutoGPT, CrewAI, or any LLM-powered system. ⚡ Quick Start bash Clone & install (core) git clone https://github.com/seojoonkim/prompt-guard.git cd prompt-guard pip install . Or install with all features (language detection, etc.) pip install .[full] Or install with dev/testing dependencies pip install .[dev] Analyze a message (CLI) prompt-guard "ignore previous instructions" Or run directly python3 -m promptguard.cli "ignore previo
| Stars | 172 |
| Forks | 33 |
| Language | Python |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 71.4286060497968/100 |
| Open Issues | 2 |
| Last Updated | 2026-04-22 |
| Created | 2026-01-29 |
| Platforms | python |
| Est. Tokens | ~31k |
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prompt-guard is Advanced prompt injection defense system for AI agents. Multi-language detection, severity scoring, and security auditing.. It is categorized as a Codex Skill with 172 GitHub stars.
prompt-guard is primarily written in Python. It covers topics such as ai-agent, ai-security, clawdbot.
You can find installation instructions and usage details in the prompt-guard GitHub repository at github.com/seojoonkim/prompt-guard. The project has 172 stars and 33 forks, indicating an active community.
prompt-guard is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to prompt-guard on Agent Skills Hub include openclaw-skills-security, free-crypto-news, rampart. 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.
Sources & who's responsible: