AgentBench: A Comprehensive Benchmark to Evaluate LLMs as Agents (ICLR'24)

by THUDM · Agent Tool · ★ 3.7k

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Security audit verdict: SAFE · quality 67/100

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.

chatgptgpt-4llmllm-agent

Quick Facts

Stars3,742
Forks280
LanguagePython
CategoryAgent Tool
LicenseApache-2.0
Quality Score67/100
Open Issues77
Last Updated2026-02-08
Created2023-07-28
Platformspython
Est. Tokens~1970k

About AgentBench

AgentBench 🌐 Leaderboard (new) 📃 Paper 👋 Join our Slack for Q & A or collaboration on next version of AgentBench! 🔥[2025.10.10] Introducing AgentBench FC (Function Calling) based on AgentRL The current repository contains the function-calling version of AgentBench, integrated with AgentRL, an end-to-end multitask and mutliturn LLM Agent RL framework.

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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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