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 rzhub · Agent Tool · ★ 195
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is GateMem safe to install? View the security audit →
GateMem -- Benchmarking Memory Governance in Multi-Principal Shared-Memory Agents GateMem evaluates whether memory-augmented LLM agents can remain useful, enforce access control, and honor deletion requests in multi-principal shared-memory environments. 📄 Paper • 🤗 Hugging Face Dataset • 🧪 Benchmark Toolkit • 🗂 Dataset Card • 🏆 Leaderboard • 🌐 Project Page ⭐ If you find GateMem useful, please consider starring the repository to help others discover the benchmark. <p alig
| Stars | 195 |
| Forks | 4 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 67.3835852042123/100 |
| Last Updated | 2026-06-21 |
| Created | 2026-06-16 |
| Platforms | python |
| Est. Tokens | ~15k |
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GateMem is GateMem: a benchmark and evaluation toolkit for memory governance in multi-principal shared-memory LLM agents.. It is categorized as a Agent Tool with 195 GitHub stars.
GateMem is primarily written in Python. It covers topics such as agent-memory, ai-safety, benchmark.
You can find installation instructions and usage details in the GateMem GitHub repository at github.com/rzhub/GateMem. The project has 195 stars and 4 forks, indicating an active community.
GateMem is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to GateMem on Agent Skills Hub include Awesome-LLM-Eval, OpenRCA, Roy. 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: