CVE-2026-62997 — AWAITING NVD

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...

Full CISO analysis pending enrichment.

How severe is it?

CVSS 3.1

N/A

EPSS

0.4%

chance of exploitation in 30 days

Higher than 33% of all CVEs

Source: EPSS v3 — FIRST.org

Exploitation Status

No known exploitation

Sophistication

N/A

What should I do?

No patch available

Monitor for updates. Consider compensating controls or temporary mitigations.

Which compliance frameworks are affected?

Compliance analysis pending. Sign in for full compliance mapping when available.

Frequently Asked Questions

What is CVE-2026-62997?

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.

Is CVE-2026-62997 actively exploited?

No confirmed active exploitation of CVE-2026-62997 has been reported, but organizations should still patch proactively.

How to fix CVE-2026-62997?

No patch is currently available. Monitor vendor advisories for updates.

What is the CVSS score for CVE-2026-62997?

No CVSS score has been assigned yet.

What are the technical details?

Original Advisory

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.

Weaknesses (CWE)

CWE-502 Deserialization of Untrusted Data Primary CWE-502 Deserialization of Untrusted Data

CWE-502 — Deserialization of Untrusted Data: The product deserializes untrusted data without sufficiently ensuring that the resulting data will be valid.

[Architecture and Design, Implementation] If available, use the signing/sealing features of the programming language to assure that deserialized data has not been tainted. For example, a hash-based message authentication code (HMAC) could be used to ensure that data has not been modified.
[Implementation] When deserializing data, populate a new object rather than just deserializing. The result is that the data flows through safe input validation and that the functions are safe.

Source: MITRE CWE corpus.

References

Timeline

Published

September 16, 2026

Last Modified

September 17, 2026

First Seen