CoBRA AI Agent
by AISmithLab[🏆 CHI26 Best Paper] CoBRA: Reproducible control of LLM agent behavior via classic social science experiments
CoBRA is an open-source ai agent by AISmithLab, written mainly in Python, with 97 GitHub stars and the MIT license. Its code was last updated Apr 21, 2026.
Scan summary: scanned Nov 5, 2026 at commit bdd6f4f. No capabilities flagged by our checks. Scan policy
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bdd6f4f · Nov 5, 2026 ScannedStatic, secrets and dependency checks completed with no critical findings.
Passed. This check ran and found nothing it flags.Static code scanpassPassed. This check ran and found nothing it flags.
Passed. This check ran and found nothing it flags.Secrets scanpassPassed. This check ran and found nothing it flags.
Flagged. This check found an issue. See the findings below.Dependency audit143 known vulnerabilitiesfailFlagged. This check found an issue. See the findings below.
Warning. This check found something worth reading in the findings below. It is not proof of a problem.ManifestnonewarnWarning. This check found something worth reading in the findings below. It is not proof of a problem.
Flagged. This check found an issue. See the findings below.BuildfailFlagged. This check found an issue. See the findings below.
Not applicable. This check does not apply to this kind of project, or it was not run in this scan.Boot & handshaken/aNot applicable. This check does not apply to this kind of project, or it was not run in this scan.
Not applicable. This check does not apply to this kind of project, or it was not run in this scan.Network egressn/aNot applicable. This check does not apply to this kind of project, or it was not run in this scan.
Not applicable. This check does not apply to this kind of project, or it was not run in this scan.Reproducible (Ă—3)n/aNot applicable. This check does not apply to this kind of project, or it was not run in this scan.
Findings (9)
- highHigh. Something risky that needs a close look before you install.dependencies issueDetails are available to the maintainer after claiming this listing.
- highHigh. Something risky that needs a close look before you install.dependencies issueDetails are available to the maintainer after claiming this listing.
- highHigh. Something risky that needs a close look before you install.dependencies issueDetails are available to the maintainer after claiming this listing.
- mediumMedium. Worth checking, often a vulnerable dependency or a setup problem.accelerate@1.9.0: 4 known vulnerabilitiesPYSEC-2026-3804, PYSEC-2026-3804, PYSEC-2026-3804, PYSEC-2026-3804
generator/requirements.txt - mediumMedium. Worth checking, often a vulnerable dependency or a setup problem.torch@2.9.1: 16 known vulnerabilitiesPYSEC-2026-139, PYSEC-2026-2286, GHSA-qfhq-4f3w-5fph, GHSA-rrmf-rvhw-rf47, PYSEC-2026-139 +11 more
generator/requirements.txt - mediumMedium. Worth checking, often a vulnerable dependency or a setup problem.transformers@5.9.0: 4 known vulnerabilitiesPYSEC-2026-3929, PYSEC-2026-3929, PYSEC-2026-3929, PYSEC-2026-3929
generator/requirements.txt - mediumMedium. Worth checking, often a vulnerable dependency or a setup problem.Install or build failed in a clean environmenter the following options: 1. set up custom discovery (`find` directive with `include` or `exclude`) 2. use a `src-layout` 3. explicitly set `py_modules` or `packages` with a list of names To find more information, look for "package discovery" on setuptools docs.
- lowLow. Minor, or common in normal tools. Shown for information.idna@3.9.0: 3 known vulnerabilitiesPYSEC-2026-215, PYSEC-2026-215, PYSEC-2026-215
generator/requirements.txt - lowLow. Minor, or common in normal tools. Shown for information.No manifest declaring network access and environment variables (agent.json)
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