LLM_MultiAgents_Survey_Papers: Large Language Model based Multi-Agents: A Survey of Progress and Challenges (In IJCAI 2024)

by taichengguo · Agent Tool · ★ 1.3k

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Security audit verdict: SAFE · quality 51/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

agentlarge-language-modelsllmllmsllms-reasoningmulti-agentssurvey

Quick Facts

Stars1,335
Forks73
CategoryAgent Tool
Quality Score51/100
Open Issues2
Last Updated2026-11-07
Created2024-01-05
Est. Tokens~20k

About LLM_MultiAgents_Survey_Papers

🤖 Awesome LLM-based Multi-Agents Papers 🔥 Paper 🔥 🔥Our Survey Paper Our survey about LLM based Multi-Agents is available at: https://arxiv.org/abs/2402.01680 Our summarized LLM-based Multi-Agents architecture is: The Overview table is as follows. More details can be seen in our paper. Very appreciate any suggestions.

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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:

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