Code execution via untrusted PyFunc CloudPickle load
Published Jun 4, 2024 · Updated Aug 2, 2024
Unsafe deserialization in MLflow 0.9.0 and later allows remote attackers to execute code through a malicious PyFunc model. The _load_context_model_and_signature function in mlflow/pyfunc/model.py passes the model's serialized Python object directly to cloudpickle.load without validating its origin or contents. Execution occurs with the end user's process privileges only after that user loads the attacker-supplied model.
Summary
What happened
Unsafe deserialization in MLflow 0.9.0 and later allows remote attackers to execute code through a malicious PyFunc model. The _load_context_model_and_signature function in mlflow/pyfunc/model.py passes the model's serialized Python object directly to cloudpickle.load without validating its origin or contents. Execution occurs with the end user's process privileges only after that user loads the attacker-supplied model.
The record
- CVE
- CVE-2024-37054
- 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=0.9.0 <=*
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 PyFunc CloudPickle 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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