Code execution via unsafe TensorFlow custom-object loading
Published Jun 4, 2024 · Updated Aug 2, 2024
Unsafe deserialization in MLflow 2.0.0rc0 and later allows remote attackers to execute code via a malicious TensorFlow model. The _load_custom_objects function in mlflow/tensorflow/init.py opens a model's serialized custom-objects file and passes it directly to cloudpickle.load without validating its contents. Execution requires a user to load the attacker-supplied model, and the embedded code runs with that user's process privileges.
Summary
What happened
Unsafe deserialization in MLflow 2.0.0rc0 and later allows remote attackers to execute code via a malicious TensorFlow model. The _load_custom_objects function in mlflow/tensorflow/init.py opens a model's serialized custom-objects file and passes it directly to cloudpickle.load without validating its contents. Execution requires a user to load the attacker-supplied model, and the embedded code runs with that user's process privileges.
The record
- CVE
- CVE-2024-37057
- 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
Compare your product and version with the public record. A matching version still requires validation against your environment.
Is a vulnerable build present?
Compare these published version ranges with your installed build and any vendor patches.
- Affected versionversion=2.0.0rc0 <=*
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
- Cloudpickle Load on TensorFlow Keras 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
See the directory against your own environment.
Your stack
Check the software in your environment
Book a demo to see how Hinoki identifies affected software and validates exploitability in your environment.
Book a demo