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CRITICAL severity

GHSA-r5qj-cvf9-p85h — pytorch-lightning

CRITICALFix: PyTorchLightning/pytorch-lightning#12212

GHSA-r5qj-cvf9-p85h is a critical-severity (CVSS 9.8) Code Injection vulnerability in pytorch-lightning. 1 public exploit reference exists, so weaponization risk is real. A fix is available for pytorch-lightning — see the affected versions and patch details below.

Code Injection in PyTorch Lightning

Also known asCVE-2022-0845PYSEC-2022-181PYSEC-2026-3969
Published
Mar 6, 2022
Updated
Sep 10, 2026
Affected
1 pkg
Patched
1 / 1
Exploits
1 known
Exploitation data as of Sep 26, 2026 · OSV.dev, NVD, FIRST.org (EPSS)

EPSS Exploitation Probability

via FIRST.org ↗
1.0%probability of exploitation in next 30 days
Lower Risk0.00%
Lower risk than most CVEs61th percentile — riskier than 61% of all scored CVEsHighest risk

Probability of exploitation in the next 30 days, from FIRST.org EPSS.

How urgent is this, really

GHSA-r5qj-cvf9-p85h by exploitation likelihood (EPSS) against impact (CVSS). Outside the shaded patch-first corner.

Where this sits among everything scored

Of 379,842 CVEs with a current EPSS score, this one falls in the < 10% band (highlighted). Counts from FIRST.org, log-scaled.

Real-World Exposure

1 pkg affected
🐍pytorch-lightning

Real-time download stats are indexed for npm and PyPI packages. This vulnerability affects PyPI packages — download data is not available via public APIs for these ecosystems.

Description

PyTorch Lightning version 1.5.10 and prior is vulnerable to code injection. An attacker could execute commands on the target OS running the operating system by setting the PL_TRAINER_GPUS when using the Trainer module. A patch is included in the 1.6.0 release.

Affected Packages

1 total 1 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPIpytorch-lightningall versions1.6.0pip install --upgrade 'pytorch-lightning==1.6.0'

Affected Products

1 product · 1 configurations
Application
pytorch lightninglightningai
< 1.6.0
range
Exploits & PoCs
1

Research use only. For defensive security, authorized penetration testing, and academic research only. Never execute exploit code against systems without explicit written authorization.

Detection & mitigation playbook

Open-source dependency
  1. Detect

    Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for pytorch-lightning, including transitive dependencies — a direct dependency you never call can still pull in a vulnerable version.

  2. Fix

    Update pytorch-lightning to 1.6.0 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-r5qj-cvf9-p85h is resolved across your whole dependency graph.

  3. Workarounds

    Stop passing untrusted input into the interpreter or shell: call the affected binary with an argument array rather than a composed command string, reject anything outside a strict allowlist of expected values, and run the component under an account that cannot reach beyond the work it legitimately does.

Frequently Asked Questions

PyTorch Lightning version 1.5.10 and prior is vulnerable to code injection. An attacker could execute commands on the target OS running the operating system by setting the `PL_TRAINER_GPUS` when using the `Trainer` module. A [patch](https://github.com/pytorchlightning/pytorch-lightning/commit/8b7a12c52e52a06408e9231647839ddb4665e8ae) is included in the `1.6.0` release.
O3 Security · Impact-Aware SCA

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GHSA-r5qj-cvf9-p85h: pytorch (Critical 9.8) | O3 Security