TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger an integer division by zero undefined behavior in `tf.raw_ops.QuantizedBiasAdd`. This is because the implementation of the Eigen kernel(https://github.com/tensorflow/tensorflow/blob/61bca8bd5ba8a68b2d97435ddfafcdf2b85672cd/tensorflow/core/kernels/quantization_utils.h#L812-L849) does a division by the number of elements of the smaller input (based on shape) without checking that this is not zero. 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.
This vulnerability carries a LOW severity rating with a CVSS v3.1 score of 2.5, requiring local system access to exploit but requires specific conditions to be met without requiring user interaction requiring only low-level privileges . The vulnerability impacts and limited availability for affected systems. Impacting 1 product from google organizations running these solutions should prioritize assessment and patching.
Reported in 2021, this vulnerability emerged during an era marked by increased sophistication in supply chain attacks, cloud infrastructure vulnerabilities, and software-as-a-service (SaaS) security challenges. Security practices during this period emphasized zero-trust architectures, container security, and API protection.
2021-05-14T20:15:12.717
2026-06-17T03:47:52.970
Modified
CVSSv3.1: 2.5 (LOW)
AV:L/AC:L/Au:N/C:P/I:P/A:P
3.9
6.4
| Type | Vendor | Product | Version/Range | Vulnerable? |
|---|---|---|---|---|
| Application | tensorflow | < 2.1.4 | Yes | |
| Application | tensorflow | < 2.2.3 | Yes | |
| Application | tensorflow | < 2.3.3 | Yes | |
| Application | tensorflow | < 2.4.2 | Yes |
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