CVE-2021-29569 (GCVE-0-2021-29569)

Vulnerability from cvelistv5 – Published: 2021-05-14 19:16 – Updated: 2024-08-03 22:11
VLAI?
Title
Heap out of bounds read in `RequantizationRange`
Summary
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.
Severity ?
2.5 (Low)
CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L
CWE
Assigner
References
Impacted products
Vendor Product Version
tensorflow tensorflow Affected: < 2.1.4
Affected: >= 2.2.0, < 2.2.3
Affected: >= 2.3.0, < 2.3.3
Affected: >= 2.4.0, < 2.4.2
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  }
}


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  • Seen: The vulnerability was mentioned, discussed, or observed by the user.
  • Confirmed: The vulnerability has been validated from an analyst's perspective.
  • Published Proof of Concept: A public proof of concept is available for this vulnerability.
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  • Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.



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