CVE-2021-29549
An attacker can cause a runtime division by zero error and denial of service in `tf.raw_ops.QuantizedBatchNormWithGlobalNormalization`.
Does this matter?
Lower severity and a low EPSS score (0.19%). Track it; it rarely justifies an emergency change on its own.
Description
TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a runtime division by zero error and denial of service in `tf.raw_ops.QuantizedBatchNormWithGlobalNormalization`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/6f26b3f3418201479c264f2a02000880d8df151c/tensorflow/core/kernels/quantized_add_op.cc#L289-L295) computes a modulo operation without validating that the divisor is not zero. Since `vector_num_elements` is determined based on input shapes(https://github.com/tensorflow/tensorflow/blob/6f26b3f3418201479c264f2a02000880d8df151c/tensorflow/core/kernels/quantized_add_op.cc#L522-L544), a user can trigger scenarios where this quantity is 0. 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.
- CVSS 3.1
- 5.5 MEDIUMCVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
- EPSS
- 0.19% probability · 9th percentile
- CISA KEV
- Not listed
- Weakness
- CWE-369
- Affected
- google/tensorflow
- Source
- security-advisories@github.com
References
- https://github.com/tensorflow/tensorflow/commit/744009c9e5cc5d0447f0dc39d055f917e1fd9e16Patch, Third Party Advisory
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x83m-p7pv-ch8vExploit, Patch, Third Party Advisory
- https://github.com/tensorflow/tensorflow/commit/744009c9e5cc5d0447f0dc39d055f917e1fd9e16Patch, Third Party Advisory
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x83m-p7pv-ch8vExploit, Patch, Third Party Advisory
Source: NVD record, EPSS from FIRST.org, KEV from CISA. Refreshed daily.