CVE-2026-62997 (GCVE-0-2026-62997)
Vulnerability from cvelistv5 – Published: 2026-09-16 20:48 – Updated: 2026-09-17 14:55
VLAI
EPSS
VEX
Title
Kedro-Datasets: Remote code execution in experimental `PyTorchDataset` via unsafe `torch.load`
Summary
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.
Severity
SSVC
Exploitation: none
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-09-17 14:55 UTC
CWE
- CWE-502 - Deserialization of Untrusted Data
Assigner
References
5 references
| URL | Tags |
|---|---|
| https://github.com/kedro-org/kedro-plugins/securi… | x_refsource_CONFIRM |
| https://github.com/kedro-org/kedro-plugins/issues/1431 | x_refsource_MISC |
| https://github.com/kedro-org/kedro-plugins/pull/1433 | x_refsource_MISC |
| https://github.com/kedro-org/kedro-plugins/commit… | x_refsource_MISC |
| https://github.com/kedro-org/kedro-plugins/releas… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| kedro-org | kedro-plugins |
Affected:
>= 5.0.0, < 9.5.0
|
guessed |
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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