CVE-2022-29211 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, the implementation of `tf.histogram_fixed_width` is vulnerable to a crash when the values array contain `Not a Number` (`NaN`) elements. The implementation assumes that all floating point operations are defined and then converts a floating point result to an integer index. If `values` contains `NaN` then the result of the division is still `NaN` and the cast to `int32` would result in a crash. This only occurs on the CPU implementation. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.
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Analysis
by VulDB Data Team • 05/27/2022
The vulnerability identified as CVE-2022-29211 affects TensorFlow's histogram_fixed_width function across multiple versions prior to 2.9.0, 2.8.1, 2.7.2, and 2.6.4. This issue represents a critical reliability concern within the machine learning framework's numerical processing capabilities, specifically impacting the CPU implementation of floating point arithmetic operations. The flaw manifests when the function processes arrays containing Not a Number (NaN) values, which are standard IEEE 754 floating point representations indicating undefined or unrepresentable numerical results. The vulnerability stems from inadequate input validation and error handling within the histogram computation logic, creating a path for denial of service conditions that can terminate application execution.
The technical implementation flaw occurs in the tf.histogram_fixed_width function where the code assumes all floating point operations produce valid results before attempting to convert these values to integer indices for histogram binning. When NaN values are present in the input array, the mathematical operations involved in computing the histogram bin indices result in NaN outputs that cannot be properly converted to the expected int32 data type. This type conversion failure triggers a crash condition that terminates the executing process. The vulnerability is classified as a software error in the handling of floating point values and aligns with CWE-369, which addresses divide-by-zero conditions and improper handling of exceptional floating point values. The specific error condition falls under CWE-248, representing an exception handling weakness where an unhandled floating point exception leads to application termination.
The operational impact of this vulnerability extends beyond simple crash conditions to potentially disrupt machine learning workflows and training processes that rely on histogram computations. Attackers could exploit this weakness by injecting NaN values into input data streams, causing targeted service disruption in environments where TensorFlow is used for model training or inference. The vulnerability affects systems where TensorFlow is deployed in production environments, particularly those processing large datasets where NaN values might inadvertently appear due to data corruption, sensor malfunctions, or mathematical operations that produce undefined results. This weakness creates a potential attack surface that could be leveraged in denial of service scenarios against machine learning applications, especially in cloud environments where TensorFlow serves as a core component of AI infrastructure. The issue is particularly concerning given TensorFlow's widespread adoption across enterprise and research environments where continuous operation is critical.
Mitigation strategies for CVE-2022-29211 require immediate deployment of patched TensorFlow versions 2.9.0, 2.8.1, 2.7.2, or 2.6.4, which contain the necessary code modifications to properly handle NaN values in the histogram computation. Organizations should implement input validation procedures to detect and filter NaN values before processing through TensorFlow functions, particularly in data pipelines that might receive untrusted or malformed input data. The patch addresses the root cause by ensuring proper handling of floating point exceptions and implementing robust error checking before type conversion operations. Security teams should also consider implementing monitoring for abnormal application termination patterns and establish automated testing procedures that include NaN value injection to validate the resilience of machine learning pipelines. Additionally, the fix aligns with ATT&CK technique T1499.004, which involves testing for system resilience against resource exhaustion and application crashes, making this vulnerability relevant to broader security posture assessments. Organizations should conduct comprehensive testing to ensure that the patched versions maintain expected performance characteristics while providing the necessary defensive capabilities against this specific class of numerical processing errors.