Severity by source
CVSS:4.0/AV:N/AC:L/AT:P/PR:N/UI:P/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X
AC:H and UI:R reflect the multiple preconditions (torch<2.6, attacker-influenced source, victim runs the loading pipeline); PR:N as no auth is needed and full RCE gives C/I/A:H.
Primary rating from Vendor (GitHub_M).
CVSS VectorVendor: GitHub_M
Lifecycle Timeline
4DescriptionCVE.org
Kedro-Datasets provides data connectors for Kedro. From version 5.0.0 until 9.5.0, kedro_datasets_experimental.pytorch.PyTorchDataset in kedro-datasets loads .pt model files with torch.load without enforcing weights_only=True, and user-supplied load_args are silently dropped. On PyTorch versions earlier than 2.6, a malicious pickle-backed model from an attacker-influenced shared registry, downloaded checkpoint, or partitioned external source can execute arbitrary code when a Kedro pipeline loads it. The issue affects only the opt-in kedro_datasets_experimental component and does not affect users who load only trusted files. This issue is fixed in version 9.5.0.
AnalysisAI
Unsafe deserialization in the opt-in kedro_datasets_experimental.pytorch.PyTorchDataset connector (kedro-datasets versions 5.0.0 through <9.5.0) allows code execution during pipeline load, because the dataset calls torch.load on .pt files without enforcing weights_only=True and silently drops user-supplied load_args, so even an explicit weights_only override never reached PyTorch. Exploitation requires a PyTorch runtime earlier than 2.6 (where weights_only defaults to False) plus a victim pipeline that loads a checkpoint obtained from an attacker-influenced shared model registry, downloaded checkpoint, or partitioned external dataset; unauthenticated remote attackers can plant the payload but must wait for that user interaction, and pipelines loading only trusted files are unaffected. …
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Vulnerability AssessmentAI
| Exploitation | Requires ALL of: (1) use of the opt-in kedro_datasets_experimental.pytorch.PyTorchDataset connector (not the mainline kedro-datasets connectors), on an affected version 5.0.0 through <9.5.0; (2) the installed PyTorch runtime is earlier than 2.6 (on torch>=2.6 weights_only=True is the upstream default and blocks the pickle RCE path); and (3) the pipeline loads a .pt file from an attacker-influenced source - a shared model registry, a downloaded checkpoint, or a partitioned external dataset. … Additional conditions and limiting factors are described in the full assessment. |
| Risk Assessment | This is a genuine but conditionally-gated remote code execution flaw (CWE-502 unsafe deserialization). … Full risk analysis with EPSS, KEV, and SSVC signal comparison available after sign-in. |
| Exploit Scenario | Full exploit scenario with step-by-step reproduction available after sign-in. |
| Remediation | Vendor-released patch: kedro-datasets 9.5.0 - upgrade to it (pip install 'kedro-datasets>=9.5.0'), which enforces weights_only=True by default and correctly forwards user load_args and save_args to torch.load/torch.save; see the advisory GHSA-f9q4-h45w-jrrq and the release at https://github.com/kedro-org/kedro-plugins/releases/tag/kedro-datasets-9.5.0. … Detailed patch versions, workarounds, and compensating controls in full report. |
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Vendor-released patch: 9.5.0. …
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External POC / Exploit Code
Leaving vuln.today
EUVD-2026-81264