Flagged: sudo usage. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by elsechord · MCP Server · ★ 54
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is CyberGuard safe to install? View the security audit →
CyberGuard 60-second demo (console walk-through): cyberguard-demo-60s.mp4 — release asset of v0.14.0. CyberGuard is an evidence-driven autonomous security operations team built on AgentTeams. It coordinates specialized Agents for alert fusion, threat intelligence, network hunting, endpoint forensics, response planning, controlled execution and independent recovery verification. The project is designed for the GOAI “Agent Infra 新智基座” track. It intentionally reuses AgentTeams for orchestration, Matrix collaboration, Skill distribution, shared storage and credential brokering, while CyberGuard provides the security-domai
| Stars | 54 |
| Forks | 1 |
| Language | Python |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 62.1550544873698/100 |
| Last Updated | 2026-09-24 |
| Created | 2026-09-17 |
| Platforms | mcp, python |
| Est. Tokens | ~18k |
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CyberGuard is Evidence-driven autonomous SOC team on AgentTeams: hash-bound approvals, HMAC audit chain, rollback and reproducible attack scenarios — auditable governance primitives for agent infrastructure.. It is categorized as a MCP Server with 54 GitHub stars.
CyberGuard is primarily written in Python. It covers topics such as agent-infra, agentscope, agentteams.
You can find installation instructions and usage details in the CyberGuard GitHub repository at github.com/elsechord/CyberGuard. The project has 54 stars and 1 forks, indicating an active community.
CyberGuard is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to CyberGuard on Agent Skills Hub include AI-SOC-Agent, Wazuh-MCP-Server, Valhuntir. 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: