Code execution via unsafe YAML hyperparameter loading
Published Dec 23, 2021 · Updated Aug 3, 2024
Deserialization in PyTorch Lightning before 1.6 allows local users to execute code by supplying a crafted YAML hyperparameter file. The load_hparams_from_yaml function passes the opened configuration file to yaml.load with UnsafeLoader, permitting Python object construction from YAML content. A user or workflow must load an attacker-controlled hyperparameter file, and resulting code runs with the consuming process's permissions.
Summary
What happened
Deserialization in PyTorch Lightning before 1.6 allows local users to execute code by supplying a crafted YAML hyperparameter file. The load_hparams_from_yaml function passes the opened configuration file to yaml.load with UnsafeLoader, permitting Python object construction from YAML content. A user or workflow must load an attacker-controlled hyperparameter file, and resulting code runs with the consuming process's permissions.
The record
- CVE
- CVE-2021-4118
- Published
- Dec 23, 2021
- Updated
- Aug 3, 2024
- Vendor
- Lightning AI
- Product
- PyTorch Lightning
- Classifications
- CWE-502, T1204.002
- Attack vector
- local
- Privileges
- unauthenticated
Timeline
How it unfolded
- Dec 23, 2021CVE publishedPublication date reported by the CVE source.
- Aug 3, 2024Record updatedLatest update available in the CVE record.
Exploitability
Present is not the same as exploitable
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Is a vulnerable build present?
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- Affected versionversion=unspecified <1.6
What conditions does exploitation require?
What is affected?
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.
Public exploit references
No public exploit references are available in this record.
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Technologies
Your stack
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Your stack
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