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 LucidAkshay · Agent Tool · ★ 253
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is kavach safe to install? View the security audit →
🛡️ KAVACH Tactical AI Workspace Monitor & EDR Kavach (Sanskrit for "Armor") is an open source Endpoint Detection and Response (EDR) layer designed exclusively to restrain, monitor, and remediate autonomous AI agents and local LLMs operating on your machine. 🛰️ The Threat & The Observer Autonomous AI agents are revolutionary, but they are inherently volatile. When they operate on your local file system, they move at superhuman speeds. A single hallucination can delete production databases, overwrite source code, or exfiltrate private API keys. Architecture Transparency: Kavach v1.1.0 operates as a high performance asy
| Stars | 253 |
| Forks | 44 |
| Language | TypeScript |
| Category | Agent Tool |
| License | GPL-3.0 |
| Quality Score | 52.6298436821199/100 |
| Last Updated | 2026-03-19 |
| Created | 2026-03-14 |
| Platforms | node |
| Est. Tokens | ~579k |
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kavach is Tactical AI Workspace Monitor & EDR. It is categorized as a Agent Tool with 253 GitHub stars.
kavach is primarily written in TypeScript. It covers topics such as agents, ai, ai-agent.
You can find installation instructions and usage details in the kavach GitHub repository at github.com/LucidAkshay/kavach. The project has 253 stars and 44 forks, indicating an active community.
kavach is released under the GPL-3.0 license, making it free to use and modify according to the license terms.
The top alternatives to kavach on Agent Skills Hub include huddol, Awesome-AI-For-Security, umadev. 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: