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 zidniryi · Agent Tool · ★ 96
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is unbagrnd safe to install? View the security audit →
unbagrnd A small desktop app that uses AI to remove the background from images — entirely on your device. No cloud API, no account, no telemetry, and no internet access required after a one-time setup step. On-device AI. Background removal runs through a real neural network (ONNX Runtime), inferred locally on your machine. No image, filename, or metadata is ever sent anywhere. 100% free & open source. MIT-licensed, built on a free, open-source stack — no API keys, no paid tiers, no usage limits. Pick your AI model. Choose from several background-removal models in Settings, trading off speed vs. accuracy. Each downloads once on first use (starting at 43 MB for the default) and is cached locally after that. Offline after setup. The only network requests the app ever makes are those one-time model downloads. N
| Stars | 96 |
| Forks | 11 |
| Language | Rust |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 67.256496162011/100 |
| Last Updated | 2026-09-18 |
| Created | 2026-08-24 |
| Platforms | rust |
| Est. Tokens | ~24k |
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unbagrnd is Unbagrnd - Free, Fast & Open-source AI-powered background remover for Windows, macOS, and Linux.. It is categorized as a Agent Tool with 96 GitHub stars.
unbagrnd is primarily written in Rust. It covers topics such as ai, ai-tools, background-removal.
You can find installation instructions and usage details in the unbagrnd GitHub repository at github.com/zidniryi/unbagrnd. The project has 96 stars and 11 forks, indicating an active community.
unbagrnd is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to unbagrnd on Agent Skills Hub include daymon, LocalLens, photoshop-mcp. 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: