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 loomax-labs · Agent Tool · ★ 112
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is InferrLM safe to install? View the security audit →
InferrLM (Previously Inferra) InferrLM is a mobile application that brings LLMs & SLMs directly to your Android & iOS device and lets your device act as a local server. Cloud-based models like Claude, Gemini and ChatGPT are also supported. File attachments with RAG are also well-supported for local models. If you want to support me and the development of this project, you can donate to me through Ko-fi. Demo Demos of using the Apple Foundation Model and downloading MLX models from HuggingFace and running them on-device. <img src="assets/demo1.gif" alt="Demo 1" h
| Stars | 112 |
| Forks | 24 |
| Language | TypeScript |
| Category | Agent Tool |
| License | AGPL-3.0 |
| Quality Score | 70.7380747490503/100 |
| Last Updated | 2026-09-06 |
| Created | 2025-02-09 |
| Platforms | gemini, node |
| Est. Tokens | ~16k |
These tools work well together with InferrLM for enhanced workflows:
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InferrLM is InferrLM - On-device AI for iOS & Android. It is categorized as a Agent Tool with 112 GitHub stars.
InferrLM is primarily written in TypeScript. It covers topics such as anthropic, document-processing, edge-ai.
You can find installation instructions and usage details in the InferrLM GitHub repository at github.com/loomax-labs/InferrLM. The project has 112 stars and 24 forks, indicating an active community.
InferrLM is released under the AGPL-3.0 license, making it free to use and modify according to the license terms.
The top alternatives to InferrLM on Agent Skills Hub include InferrLM, agents.cpp, agents-cpp-sdk. 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: