VisualAgentBench: Towards Large Multimodal Models as Visual Foundation Agents

by THUDM · Agent Tool · ★ 278

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Security audit verdict: SAFE · quality 69/100

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.

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.

gptllm-agentmultimodal-large-language-models

Quick Facts

Stars278
Forks14
LanguagePython
CategoryAgent Tool
LicenseApache-2.0
Quality Score69/100
Open Issues17
Last Updated2025-04-24
Created2024-08-08
Platformspython
Est. Tokens~378k

About VisualAgentBench

VisualAgentBench (VAB) 🌐 Website 🗂️ VAB Training (ModelScope) VisualAgentBench: Towards Large Multimodal Models as Visual Foundation Agents VisualAgentBench (VAB) is the first benchmark designed to systematically evaluate and develop large multi models (LMMs) as visual foundation agents, which comprises 5 distinct environments across 3 types of representative visual agent tasks (Embodied, GUI,...

Compatible Skills

These tools work well together with VisualAgentBench for enhanced workflows:

  • multimind-sdk — semantic(0.31)+complementary+rare_topics+same_lang+similar_pop+shared_platform (60%)
  • MLLM-Tool — semantic(0.34)+complementary+same_lang+similar_pop+shared_platform (57%)
  • OpenAdapt — semantic(0.34)+complementary+same_lang+similar_pop+shared_platform (57%)
  • SimplerLLM — semantic(0.30)+complementary+same_lang+similar_pop+shared_platform (56%)
  • multimodal-agents-course — semantic(0.27)+complementary+same_lang+similar_pop+shared_platform (54%)

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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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