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 NuGuardAI · MCP Server · ★ 57
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is nuguard safe to install? View the security audit →
nuguard AI-SBOM generation, static analysis, and automated red-teaming / adversarial-attack-generation for AI agents and applications. What it does · See it in action · Framework coverage · Comparison · Getting started · FAQ NuGuard is an open source AI application safety & security toolkit. It generates an AI Software Bill of Materials (AI-SBOM) for your agentic application, statically analyzes it for structural risk in the AI Stack and the softw
| Stars | 57 |
| Forks | 16 |
| Language | Python |
| Category | MCP Server |
| Quality Score | 65.3457787289679/100 |
| Open Issues | 24 |
| Last Updated | 2026-10-03 |
| Created | 2026-03-20 |
| Platforms | mcp, python |
| Est. Tokens | ~15k |
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nuguard is AI red-teaming tool and LLM security framework to evaluate agentic AI applications. Tests prompt injections, handles vulnerability assessment, SBOM generation, and static analysis.. It is categorized as a MCP Server with 57 GitHub stars.
nuguard is primarily written in Python. It covers topics such as adversarial-machine-learning, agentic-ai, ai-agents.
You can find installation instructions and usage details in the nuguard GitHub repository at github.com/NuGuardAI/nuguard. The project has 57 stars and 16 forks, indicating an active community.
The top alternatives to nuguard on Agent Skills Hub include agent-audit, guardana, agent-security-scanner-mcp. 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: