CoBRA: [ CHI26 Best Paper] CoBRA: Reproducible control of LLM agent behavior via classic social science experiments

by AISmithLab · Agent Tool · ★ 97

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Security audit verdict: SAFE · quality 62/100

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.

agent-based-modelingai-agentai-toolshcillm-agentllm-behaviorrepresentation-engineeringrepresentation-learningreproducible-researchsocial-science

Quick Facts

Stars97
Forks4
LanguagePython
CategoryAgent Tool
LicenseMIT
Quality Score62/100
Open Issues2
Last Updated2026-04-21
Created2025-10-25
Platformspython
Est. Tokens~567k

About CoBRA

Toward Precise and Consistent Agent Behaviors across Models Anchored by Validated Social Science Knowledge 🌐 Project Page: cobra.clawder.ai  |  📄 Paper: arXiv 2509.13588 If you find CoBRA useful, please star ⭐ this repo to help others discover it! 💡 What is Cognitive Bias? Systematic deviations from rat

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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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