CVE-2022-29216 in TensorFlow
Summary
by MITRE • 05/21/2022
TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, TensorFlow's `saved_model_cli` tool is vulnerable to a code injection. This can be used to open a reverse shell. This code path was maintained for compatibility reasons as the maintainers had several test cases where numpy expressions were used as arguments. However, given that the tool is always run manually, the impact of this is still not severe. The maintainers have now removed the `safe=False` argument, so all parsing is done without calling `eval`. The patch is available in versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4.
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Analysis
by VulDB Data Team • 05/27/2022
The vulnerability identified as CVE-2022-29216 resides within TensorFlow's saved_model_cli tool, a command-line interface designed for managing and inspecting saved models. This tool serves as a critical component for developers working with machine learning models, providing functionality to load, inspect, and manipulate saved TensorFlow models. The vulnerability stems from the tool's reliance on unsafe evaluation mechanisms that were historically maintained for backward compatibility with existing test cases involving numpy expressions. The security flaw manifests in the tool's handling of user-provided arguments, where it inadvertently processes code expressions through potentially dangerous evaluation paths.
The technical implementation of this vulnerability involves the use of the `safe=False` parameter in the parsing logic, which enables the execution of arbitrary Python code through the eval function. This creates a code injection vector where malicious inputs can be interpreted and executed as Python commands. Attackers could exploit this by crafting specially formatted arguments that, when processed by the saved_model_cli tool, would trigger the evaluation of malicious code. The most severe exploitation scenario involves the potential to establish reverse shells, allowing remote attackers to gain unauthorized access to systems running vulnerable TensorFlow versions. This type of vulnerability falls under the CWE-94 weakness category, specifically representing an "Improper Neutralization of Special Elements used in an OS Command" and more directly aligns with CWE-74, "Improper Neutralization of Special Elements in Output Used by a Downstream Component."
The operational impact of this vulnerability extends beyond simple code execution, as it represents a significant security risk in environments where TensorFlow is used for model deployment and management. Organizations utilizing the saved_model_cli tool for routine operations face potential compromise, particularly in scenarios where the tool is executed in environments with elevated privileges or where users have the ability to provide input to the tool. The vulnerability's exploitation potential is mitigated by the fact that the tool is designed for manual execution, reducing the likelihood of automated attacks, but it remains a serious concern for environments where untrusted input might be processed. The vulnerability affects major TensorFlow release lines including 2.9.0, 2.8.1, 2.7.2, and 2.6.4, indicating a widespread impact across the platform's version history.
The remediation approach implemented by TensorFlow developers involves the complete removal of the `safe=False` argument from the parsing logic, ensuring that all argument processing occurs without invoking the eval function. This represents a fundamental architectural change that eliminates the code injection vector entirely. The fix aligns with established security practices by adopting a principle of least privilege and avoiding dangerous evaluation mechanisms in interactive tools. The patch addresses the root cause by enforcing safe parsing methods that prevent arbitrary code execution while maintaining the tool's core functionality. This solution demonstrates a proper response to the vulnerability through the removal of unsafe code patterns and aligns with the ATT&CK framework's mitigation strategies for code injection attacks. The updated versions of TensorFlow ensure that the saved_model_cli tool operates in a secure mode that prevents the execution of arbitrary code through command-line arguments, effectively closing the exploitation pathway while maintaining backward compatibility for legitimate use cases. Organizations should immediately upgrade to the patched versions to eliminate the risk of exploitation and ensure their machine learning environments remain secure against this specific class of code injection vulnerabilities.