Arbitrary code execution via untrusted model deserialization
Published Jun 4, 2024 · Updated Aug 2, 2024
Unsafe deserialization in MLflow 1.1.0 and later allows remote attackers to execute arbitrary code through a malicious scikit-learn model. The mlflow.sklearn.load_model path reaches _load_model_from_local_file, which passes model bytes to cloudpickle.load or pickle.load without establishing trust. Execution occurs only when an end user loads the attacker-supplied model, and the code runs with that user's privileges.
Summary
What happened
Unsafe deserialization in MLflow 1.1.0 and later allows remote attackers to execute arbitrary code through a malicious scikit-learn model. The mlflow.sklearn.load_model path reaches _load_model_from_local_file, which passes model bytes to cloudpickle.load or pickle.load without establishing trust. Execution occurs only when an end user loads the attacker-supplied model, and the code runs with that user's privileges.
The record
- CVE
- CVE-2024-37052
- Published
- Jun 4, 2024
- Updated
- Aug 2, 2024
- Vendor
- MLflow Project
- Product
- MLflow
- Classifications
- CWE-502, T1204.002
- Attack vector
- network
- Privileges
- unauthenticated
Timeline
How it unfolded
- Jun 4, 2024CVE publishedPublication date reported by the CVE source.
- Aug 2, 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?
Compare these published version ranges with your installed build and any vendor patches.
- Affected versionversion=1.1.0
- Affected versionversion=1.1.0 <=*
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
- HiddenLayer malicious scikit-learn model proof of conceptproof of concept · demonstrated
Labels summarize the accepted research assessment. They do not indicate a test against your environment.
Technologies
Your stack
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Your stack
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