HighVulnerability

CVE-2024-34073: sagemaker-python-sdk OS command injection via capture_dependencies

Source: NVD(opens in a new tab)
Identifier: CVE-2024-34073
Published: May 3, 2024
Record updated: October 9, 2026

Affected: sagemaker < 2.214.3
Fixed in: 2.214.3
released April 3, 2024, 29 days before disclosure
Known exploitation: Not listed in the CISA Known Exploited Vulnerabilities catalog at the last check.
EPSS: 1.2%

Summary

CVE-2024-34073 affects sagemaker-python-sdk. In affected versions, the capture_dependencies function in the sagemaker.serve.save_retrive.version_1_0_0.save.utils module allows OS command injection through the requirements_path parameter. An unprivileged third party may use this to achieve remote code execution or denial of service, affecting confidentiality and integrity.

Mitigation

Fixed in version 2.214.3; users are advised to upgrade. If upgrading is not possible, do not override the requirements_path parameter of capture_dependencies and use the default value instead.

Details

Scores and exploitation

CVSS score: 7.8(high)
Patch available: Yes
EPSS: 1.2%Estimated probability that this CVE is exploited in the next 30 days, from the FIRST Exploit Prediction Scoring System. Read on November 3, 2026.

Weaknesses and techniques

Weakness (CWE): CWE-78
Attack pattern (CAPEC): CAPEC-88

Affected

Packages: sagemaker@< 2.214.3 (fixed: 2.214.3)
Vendors: Amazon

Classification

Attack type: Other
Attack sophistication: Moderate
AI component targeted: Framework
Impact: Confidentiality,
Integrity,
Availability
Labels: security

How this record was produced

Summaries, mitigations and labels on this page are generated automatically from the source. The linked source is authoritative. The source gives this advisory a description and no title, so the title above is a headline generated from the source text.

Original source: https://nvd.nist.gov/vuln/detail/CVE-2024-34073

First tracked: February 15, 2026 at 08:53 PM ET, from NVD/CVE Database

Classifier: claude-haiku-5-5, prompt v4

Classifier confidence: 95% (self-assessed)

Affected packages in the Exposure Registry

Matched by package name and ecosystem. Each entry shows whether the package delegates to a language model and how many tracked packages depend on it.

sagemakerPyPIReaches an LLM package through dependencies · 3 tracked dependents

Summary

Mitigation

Affected packages in the Exposure Registry

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