Code execution via unsafe PyTorch checkpoint deserialization
Published Mar 24, 2026 · Updated Mar 24, 2026
Deserialization of untrusted data in NVIDIA Apex on Linux with PyTorch before 2.6 allows adjacent authenticated attackers to execute code. Apex did not require PyTorch 2.6, leaving torch.load's unsafe weights_only=False pickle behavior available for crafted checkpoints. Exploitation requires adjacent access and low privileges but no user interaction, and can execute code, alter or disclose data, or interrupt service.
Summary
What happened
Deserialization of untrusted data in NVIDIA Apex on Linux with PyTorch before 2.6 allows adjacent authenticated attackers to execute code. Apex did not require PyTorch 2.6, leaving torch.load's unsafe weights_only=False pickle behavior available for crafted checkpoints. Exploitation requires adjacent access and low privileges but no user interaction, and can execute code, alter or disclose data, or interrupt service.
The record
- CVE
- CVE-2025-33244
- Published
- Mar 24, 2026
- Updated
- Mar 24, 2026
- Vendor
- DPDK Project
- Product
- Apex
- Classifications
- CWE-502, T1059.006
- Attack vector
- adjacent
- Privileges
- authenticated
Timeline
How it unfolded
- Mar 24, 2026CVE publishedPublication date reported by the CVE source.
- Mar 24, 2026Record updatedLatest update available in the CVE record.
Exploitability
Present is not the same as exploitable
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Is a vulnerable build present?
Compare these published version ranges with your installed build and any vendor patches.
- Affected versionversion=All versions that do not include commit db8e053
What conditions does exploitation require?
What is affected?
Published CVSS scores
CVSS describes severity. EPSS estimates exploitation probability.
Attacks
What attackers are doing with it
Daily unique IPs observed by Shadowserver honeypots for known exploited vulnerabilities (KEVs). Missing observations do not establish an absence of attacks.
Weakness, pattern, technique
Public exploit references
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Technologies
Your stack
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Your stack
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