CVE-2021-29534 in TensorFlow
Summary
by MITRE • 05/15/2021
TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a denial of service via a `CHECK`-fail in `tf.raw_ops.SparseConcat`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/b432a38fe0e1b4b904a6c222cbce794c39703e87/tensorflow/core/kernels/sparse_concat_op.cc#L76) takes the values specified in `shapes[0]` as dimensions for the output shape. The `TensorShape` constructor(https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/framework/tensor_shape.cc#L183-L188) uses a `CHECK` operation which triggers when `InitDims`(https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/framework/tensor_shape.cc#L212-L296) returns a non-OK status. This is a legacy implementation of the constructor and operations should use `BuildTensorShapeBase` or `AddDimWithStatus` to prevent `CHECK`-failures in the presence of overflows. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
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Analysis
by VulDB Data Team • 05/19/2021
The vulnerability described in CVE-2021-29534 resides within the TensorFlow machine learning platform, specifically in the `tf.raw_ops.SparseConcat` operation that handles sparse tensor concatenation. This issue represents a denial of service condition that can be triggered by malicious input, making it particularly concerning for production environments where system stability is paramount. The flaw manifests when the implementation processes shape information from input tensors, specifically using dimensions from `shapes[0]` to construct the output shape. This particular implementation path invokes the `TensorShape` constructor which contains a critical `CHECK` operation that fails when dimension validation encounters overflow conditions. The vulnerability stems from a legacy implementation pattern that does not properly handle edge cases in tensor shape validation, creating a potential attack surface where an attacker can craft inputs that cause the system to terminate abruptly rather than gracefully handling the error condition.
The technical execution of this vulnerability occurs through the specific code path in `sparse_concat_op.cc` at line 76 where the output shape dimensions are derived from input tensor shapes. When the `TensorShape` constructor is called with problematic dimension values, it invokes `InitDims` which returns a non-OK status, causing the `CHECK` operation to trigger and terminate the process. This behavior aligns with CWE-682, which addresses "Incorrect Calculation" and specifically relates to improper handling of mathematical operations that can lead to system failures. The fundamental issue lies in the lack of proper error handling within the tensor shape construction process, where legacy code patterns that rely on `CHECK` operations instead of more robust status checking mechanisms are employed. The vulnerability demonstrates poor defensive programming practices where the system fails to validate input parameters adequately before proceeding with operations that could cause system instability.
The operational impact of this vulnerability extends beyond simple denial of service, as it affects the reliability and availability of machine learning platforms that depend on TensorFlow's sparse tensor operations. In production environments, this could lead to unexpected service interruptions, particularly when the system processes untrusted input data such as user-provided models or training datasets. The vulnerability affects multiple versions of TensorFlow including 2.1.4, 2.2.3, 2.3.3, 2.4.2, and the upcoming 2.5.0 release, indicating a widespread issue within the software's architecture. From an attacker perspective, this represents a low-effort method for causing system instability, as the trigger requires only specific tensor shape parameters that cause the overflow condition. The impact is consistent with ATT&CK technique T1499.004, which describes "Utilities: Endpoint Denial of Service" and highlights how seemingly minor implementation flaws can be exploited to create system-wide availability issues.
Mitigation strategies for this vulnerability focus on implementing proper error handling mechanisms and upgrading to patched versions of TensorFlow. The recommended approach involves using the more robust `BuildTensorShapeBase` or `AddDimWithStatus` operations instead of the legacy constructor that relies on `CHECK` operations. Organizations should prioritize upgrading to TensorFlow 2.5.0 or applying the cherry-picked fixes to affected versions 2.4.2, 2.3.3, 2.2.3, and 2.1.4. Additionally, input validation should be strengthened to prevent malformed tensor shapes from reaching the vulnerable code path, and defensive programming practices should be enforced throughout the codebase to avoid similar issues in other operations. The fix demonstrates the importance of maintaining modern error handling patterns in system software where legacy code can introduce critical stability vulnerabilities, particularly in environments where software components handle complex mathematical operations and data transformations.