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 cxumol · Agent Tool · ★ 148
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is promptmask safe to install? View the security audit →
PromptMask A local-first privacy layer for Large Language Models. Cloud AI is smart but sacrifices privacy. Local AI keeps your secret but is dumb. What if we can combine the advantages of both sides? -blue) PromptMask ensures your private data never leaves your machines. It redacts and un-redacts sensitive data locally, so that only anonymized data is sent to third-party AI services.() (): "Local" in this project is used only for terminology distinction, differing from Cloud AI whose privacy protection is questionable. PromptMask is fully compatible with any remote LLM that you tr
| Stars | 148 |
| Forks | 15 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 70.4071498121825/100 |
| Last Updated | 2026-01-04 |
| Created | 2025-07-30 |
| Platforms | browser, python |
| Est. Tokens | ~12k |
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promptmask is Never give AI companies your secrets! A local LLM-based privacy filter for LLM users. Seamless integration with your existing AI tools as a Python library / OpenAI SDK replacement / API Gatetway / Web. It is categorized as a Agent Tool with 148 GitHub stars.
promptmask is primarily written in Python. It covers topics such as ai, api, data-masking.
You can find installation instructions and usage details in the promptmask GitHub repository at github.com/cxumol/promptmask. The project has 148 stars and 15 forks, indicating an active community.
promptmask is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to promptmask on Agent Skills Hub include pasteguard, open-assistant-api, maskit. 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: