AI-agents-for-cybersecurity: Hands-on code, labs, and reference material by Omar Santos for building, securing, and operating AI agents

by santosomar · MCP Server · ★ 250

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 64/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

agent-memoryagent-orchestrationagent-skillsagenticagentic-aiagentic-ragagentsaiartificial-intelligencecyber

Quick Facts

Stars250
Forks101
LanguagePython
CategoryMCP Server
LicenseBSD-3-Clause
Quality Score64/100
Open Issues1
Last Updated2026-10-19
Created2024-08-06
Platformsmcp, python
Est. Tokens~16k

About AI-agents-for-cybersecurity

AI Agents for Cybersecurity Hands-on code, labs, and reference material by Omar Santos for building, securing, and operating AI agents in cybersecurity. Where this repository is used This is the companion repository for several of

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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:

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