Flagged: opens a tunnel service. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by mbzuai-oryx · Agent Tool · ★ 967
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is groundingLMM safe to install? View the security audit →
GLaMM : Pixel Grounding Large Multimodal Model [CVPR 2024] Hanoona Rasheed\, Muhammad Maaz\, Sahal Shaji, Abdelrahman Shaker, Salman Khan, Hisham Cholakkal, Rao M. Anwer, Eric Xing, Ming-Hsuan Yang and Fahad Khan Mohamed bin Zayed University of AI, Australian National University, Aalto University, Carnegie Mellon University, University of California - Merced, Linköping University, Google Research [](https://www.youtube.com/wa
| Stars | 967 |
| Forks | 56 |
| Language | Python |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 60.9276008749305/100 |
| Open Issues | 36 |
| Last Updated | 2026-09-05 |
| Created | 2023-11-02 |
| Platforms | python |
| Est. Tokens | ~15k |
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groundingLMM is [CVPR 2024 🔥] Grounding Large Multimodal Model (GLaMM), the first-of-its-kind model capable of generating natural language responses that are seamlessly integrated with object segmentation masks.. It is categorized as a Agent Tool with 967 GitHub stars.
groundingLMM is primarily written in Python. It covers topics such as foundation-models, llm-agent, lmm.
You can find installation instructions and usage details in the groundingLMM GitHub repository at github.com/mbzuai-oryx/groundingLMM. The project has 967 stars and 56 forks, indicating an active community.
groundingLMM is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to groundingLMM on Agent Skills Hub include ai-agents-from-scratch, gptme, langroid. 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.
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