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 →
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A Survey on Large Language Model-Based Game Agents (ACM CSUR) 🔥 Must-read papers for LLM-based Game agents. 📘 Our survey has been accepted by ACM Computing Surveys (CSUR). We are preparing the camera ready. Feel free to reach out if you find missing reference. 💫 We continuously update the GitHub list on a weekly basis. 📝 If you discover any papers that are suitable but not yet included, please open an issue or submit a pull request. Browse by Genre (60) (135) (47) (51) (15) (56) (17) (1) (14) (29) (3) [#benc
| Stars | 966 |
| Forks | 35 |
| Category | AI Tool |
| Quality Score | 51.6421798270055/100 |
| Open Issues | 9 |
| Last Updated | 2026-06-07 |
| Created | 2024-02-15 |
| Est. Tokens | ~12k |
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awesome-LLM-game-agent-papers is A Survey on Large Language Model-Based Game Agents (ACM CSUR). It is categorized as a AI Tool with 966 GitHub stars.
You can find installation instructions and usage details in the awesome-LLM-game-agent-papers GitHub repository at github.com/git-disl/awesome-LLM-game-agent-papers. The project has 966 stars and 35 forks, indicating an active community.
The top alternatives to awesome-LLM-game-agent-papers on Agent Skills Hub include LLM_MultiAgents_Survey_Papers, awesome-lifelong-llm-agent, ruby_llm. 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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