CVE-2021-29569
Published May 14, 2021
Last updated 3 years ago
Overview
- Description
- TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs. The implementation(https://github.com/tensorflow/tensorflow/blob/ac328eaa3870491ababc147822cd04e91a790643/tensorflow/core/kernels/requantization_range_op.cc#L49-L50) assumes that the `input_min` and `input_max` tensors have at least one element, as it accesses the first element in two arrays. If the tensors are empty, `.flat<T>()` is an empty object, backed by an empty array. Hence, accesing even the 0th element is a read outside the bounds. 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.
- Source
- security-advisories@github.com
- NVD status
- Analyzed
Social media
- Hype score
- Not currently trending
Risk scores
CVSS 3.1
- Type
- Primary
- Base score
- 7.1
- Impact score
- 5.2
- Exploitability score
- 1.8
- Vector string
- CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:H
- Severity
- HIGH
CVSS 2.0
- Type
- Primary
- Base score
- 3.6
- Impact score
- 4.9
- Exploitability score
- 3.9
- Vector string
- AV:L/AC:L/Au:N/C:P/I:N/A:P
Weaknesses
- security-advisories@github.com
- CWE-125
Configurations
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