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CVE-2021-29569
Good to know:
Date: May 14, 2021
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.
Language: Python
Severity Score
Severity Score
Weakness Type (CWE)
Out-of-bounds Read
CWE-125Top Fix
CVSS v3.1
Base Score: |
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---|---|
Attack Vector (AV): | NETWORK |
Attack Complexity (AC): | LOW |
Privileges Required (PR): | NONE |
User Interaction (UI): | NONE |
Scope (S): | UNCHANGED |
Confidentiality (C): | LOW |
Integrity (I): | LOW |
Availability (A): | NONE |
CVSS v2
Base Score: |
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---|---|
Access Vector (AV): | LOCAL |
Access Complexity (AC): | LOW |
Authentication (AU): | NONE |
Confidentiality (C): | PARTIAL |
Integrity (I): | NONE |
Availability (A): | PARTIAL |
Additional information: |