CVE-2023-5245 in MLeap
Summary
by MITRE • 11/15/2023
FileUtil.extract() enumerates all zip file entries and extracts each file without validating whether file paths in the archive are outside the intended directory.
When creating an instance of TensorflowModel using the saved_model format and an exported tensorflow model, the apply() function invokes the vulnerable implementation of FileUtil.extract().
Arbitrary file creation can directly lead to code execution
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Analysis
by VulDB Data Team • 02/26/2026
The vulnerability described in CVE-2023-5245 represents a critical directory traversal flaw within TensorFlow's model loading mechanism that enables arbitrary file creation and potential code execution. This vulnerability resides in the FileUtil.extract() method which processes zip file entries without proper validation of file paths, creating a pathway for malicious actors to manipulate the extraction process. The flaw specifically manifests when TensorFlow processes saved_model format files, which are commonly used for exporting and loading machine learning models. When a TensorflowModel instance is created with a saved_model format and the apply() function is invoked, the vulnerable code path is triggered, allowing attackers to manipulate the extraction process through specially crafted zip archives.
The technical root cause of this vulnerability aligns with CWE-22, which describes improper limitation of a pathname to a restricted directory, commonly known as directory traversal. The FileUtil.extract() implementation fails to validate whether extracted file paths fall within the intended directory boundaries, enabling attackers to create files outside of the designated extraction location. This weakness allows for path traversal attacks where malicious entries within zip archives can contain absolute paths or directory traversal sequences such as ../ that bypass normal extraction safeguards. The vulnerability is particularly dangerous because it operates at the file system level, where the extraction process can overwrite critical system files or create malicious files in sensitive locations.
The operational impact of CVE-2023-5245 extends beyond simple file creation to encompass full code execution capabilities within the context of the running TensorFlow process. When attackers exploit this vulnerability, they can place malicious binaries or scripts in directories accessible to the TensorFlow runtime, potentially leading to privilege escalation or system compromise. The vulnerability affects the saved_model format which is widely used in production environments, making it a significant risk for organizations deploying machine learning workloads. Attackers can leverage this vulnerability to execute arbitrary code on systems running vulnerable TensorFlow versions, particularly in environments where model files are loaded from untrusted sources or where automatic model loading occurs during application startup.
Security mitigations for this vulnerability should focus on implementing proper path validation and sanitization within the FileUtil.extract() method, ensuring that all extracted file paths are normalized and verified against the intended extraction directory. Organizations should apply the latest TensorFlow security patches and updates immediately, as the vulnerability affects core model loading functionality. Additionally, implementing strict file access controls and sandboxing measures around TensorFlow model loading processes can limit the potential impact of exploitation. The ATT&CK framework categorizes this vulnerability under T1059.001 for command and scripting interpreter and T1078.004 for valid accounts, as exploitation typically requires the ability to place malicious files in the target system and execute code through the TensorFlow runtime environment. Organizations should also implement network segmentation and file integrity monitoring to detect and prevent unauthorized model file modifications that could lead to exploitation of this vulnerability.