CVE-2021-41202 in TensorFlow
Summary
by MITRE • 11/06/2021
TensorFlow is an open source platform for machine learning. In affected versions while calculating the size of the output within the `tf.range` kernel, there is a conditional statement of type `int64 = condition ? int64 : double`. Due to C++ implicit conversion rules, both branches of the condition will be cast to `double` and the result would be truncated before the assignment. This result in overflows. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.
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Analysis
by VulDB Data Team • 11/10/2021
The vulnerability identified as CVE-2021-41202 resides within TensorFlow's implementation of the tf.range kernel, a fundamental component for generating sequences of numbers in machine learning workflows. This issue demonstrates a classic example of improper type handling in C++ code that leads to unintended behavior during arithmetic operations. The flaw manifests in a conditional expression where an int64 value is conditionally assigned based on a boolean condition, but the underlying C++ implicit conversion rules cause both branches of the ternary operator to be promoted to double precision floating point values before the final assignment back to int64. This conversion process creates a scenario where the intermediate double values can overflow the range of int64 integers, resulting in unexpected truncation and loss of precision that can significantly alter program behavior.
The technical implementation of this vulnerability stems from C++'s type promotion rules within conditional expressions. When the ternary operator evaluates the condition and both possible return values undergo implicit conversion to double, the arithmetic operations performed on these promoted values can exceed the maximum representable range of int64 integers. The subsequent assignment back to int64 causes truncation of the double values, leading to integer overflow conditions that can produce incorrect sequence generation results. This represents a CWE-191 Integer Underflow/Overflow vulnerability where the overflow occurs during the intermediate calculation phase rather than the final assignment, making it particularly insidious as it may not immediately manifest in obvious error conditions but instead silently corrupts data processing pipelines.
The operational impact of this vulnerability extends beyond simple mathematical errors to potentially compromise entire machine learning workflows that depend on correct sequence generation. When tf.range is used in neural network training loops, data preprocessing pipelines, or any application requiring systematic number generation, incorrect output sizes can lead to memory allocation failures, data corruption, or algorithmic instability. Attackers could potentially exploit this vulnerability to cause denial of service conditions by crafting inputs that trigger specific overflow scenarios, or in more sophisticated attacks, manipulate the sequence generation to introduce subtle biases in machine learning models. The vulnerability affects multiple TensorFlow versions including 2.4.4, 2.5.2, 2.6.1, and the unpatched 2.7.0 release, indicating this is a widespread issue across the supported release lifecycle.
Mitigation strategies for CVE-2021-41202 should prioritize immediate patching of affected TensorFlow installations to versions 2.4.4, 2.5.2, 2.6.1, and 2.7.0 where the fix has been implemented. Organizations should conduct thorough vulnerability assessments to identify all systems running affected TensorFlow versions and ensure proper patch management protocols are in place. The fix addresses the core issue by modifying the conditional expression to prevent the unintended promotion to double precision values, maintaining proper integer arithmetic throughout the calculation process. Security teams should also implement monitoring for unusual memory allocation patterns or unexpected behavior in machine learning pipelines that might indicate exploitation attempts. This vulnerability aligns with ATT&CK technique T1059.001 Command and Scripting Interpreter and T1499.004 Endpoint Denial of Service, as exploitation could lead to system instability and service disruption in machine learning environments. Additionally, the vulnerability demonstrates the importance of proper type safety in C++ applications and highlights the need for comprehensive testing of arithmetic operations in security-critical software components.