CVE-2021-41201 in TensorFlow
Summary
by MITRE • 11/06/2021
TensorFlow is an open source platform for machine learning. In affeced versions during execution, `EinsumHelper::ParseEquation()` is supposed to set the flags in `input_has_ellipsis` vector and `*output_has_ellipsis` boolean to indicate whether there is ellipsis in the corresponding inputs and output. However, the code only changes these flags to `true` and never assigns `false`. This results in unitialized variable access if callers assume that `EinsumHelper::ParseEquation()` always sets these flags. 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-2021-41201 resides within the TensorFlow machine learning platform, specifically within the EinsumHelper::ParseEquation() function that handles equation parsing for einsum operations. This flaw represents a classic case of uninitialized variable usage that can lead to unpredictable behavior and potential security implications within the machine learning inference pipeline. The affected versions of TensorFlow demonstrate a critical oversight in variable initialization where flags intended to indicate the presence of ellipsis notation in input tensors and output tensors are only ever set to true values without proper initialization to false. This design flaw creates a scenario where downstream code that relies on these flags for logical decision-making may encounter undefined behavior when the flags retain their previous values from prior function calls or memory states.
The technical implementation of this vulnerability stems from improper initialization of boolean flags within the ParseEquation function, which is part of the einsum operation processing framework. According to CWE-457, this represents a use of uninitialized variable vulnerability where the input_has_ellipsis vector and output_has_ellipsis boolean are not properly initialized before being used in conditional logic. The function's logic only includes code paths that set these flags to true but lacks the necessary initialization to false for cases where ellipsis notation is not present. This creates a scenario where the function's behavior becomes dependent on the memory state of previously executed code, making the system's response unpredictable and potentially exploitable.
The operational impact of this vulnerability extends beyond simple functional instability into potential security implications for machine learning environments that rely on TensorFlow's einsum operations. When callers of ParseEquation assume that the function will always properly initialize these flags, they may execute conditional code paths based on incorrect flag values, potentially leading to incorrect tensor operations or memory access violations. This vulnerability particularly affects TensorFlow versions 2.4.0 through 2.6.0, with the fix being incorporated into TensorFlow 2.7.0 and backported to older supported versions. The vulnerability can be exploited in environments where machine learning models perform complex tensor operations, especially in production systems where reliability and predictable behavior are critical.
The mitigation strategy for CVE-2021-41201 involves updating to TensorFlow 2.7.0 or applying the relevant backport fixes to affected versions 2.6.1, 2.5.2, and 2.4.4. This remediation addresses the core issue by ensuring proper initialization of the boolean flags within the ParseEquation function. From an ATT&CK perspective, this vulnerability could be leveraged in a privilege escalation scenario if exploited within a machine learning pipeline that processes untrusted data, potentially allowing an attacker to manipulate tensor operations and influence model outputs. The fix aligns with security best practices by ensuring proper variable initialization and preventing undefined behavior in the mathematical operations that form the foundation of machine learning computations. Organizations should prioritize updating their TensorFlow installations to prevent potential exploitation and maintain the integrity of their machine learning workflows.