SOC status:Duty analyst on shift

UK Cyber Defence
VulnerabilityModified

CVE-2021-29569

The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs.

HIGH 7.1EPSS 0.20%

Does this matter?

High impact if exploited, but EPSS currently rates exploitation as unlikely (0.20%). Schedule it in the normal patch cycle and watch for a rise in EPSS or a public exploit.

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.

CVSS 3.1
7.1 HIGHCVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:H
EPSS
0.20% probability · 10th percentile
CISA KEV
Not listed
Weakness
CWE-125
Affected
google/tensorflow
Source
security-advisories@github.com

References

Source: NVD record, EPSS from FIRST.org, KEV from CISA. Refreshed daily.