CyberSecBulletin
CVE-2022-21728NVD status: Modified

Tensorflow: Out-of-bounds Read

Published Updated By the CyberSecBulletin desk

CVE-2022-21728 is a high-severity out-of-bounds read vulnerability in Tensorflow from Google, with a CVSS 3.1 base score of 8.1 out of 10. It can be exploited remotely over the network by an attacker with a low-privileged account, without any action from a user. Affected Tensorflow versions: 2.5.2 and earlier; 2.6.0 through 2.6.2; 2.7.0. The record does not name a fixed version, so check the vendor advisory. There is no confirmed exploitation so far. EPSS estimates a 1.13% chance of exploitation within 30 days, higher than 67% of scored CVEs.

Next patch cycle

High severity with no evidence of exploitation so far.

What to do

  1. Check which version of Tensorflow you run against the affected versions below, and install the vendor's latest release.
  2. Read the advisory on github.com for workarounds and exact fixed builds.

Affected products and versions

VendorProductAffected versionsFixed in
GoogleTensorflow2.5.2 and earlier; 2.6.0 through 2.6.2; 2.7.0Not stated

Version data comes from NVD's product matching. Confirm against the vendor advisory before deciding you are unaffected.

How the severity score breaks down

Attack vector
NetworkCan be exploited remotely over a network, including the internet.
Attack complexity
LowNo special conditions are needed; the attack works reliably.
Privileges required
LowA basic user account is enough.
User interaction
NoneNo action by a victim is required.
Scope
UnchangedImpact stays within the vulnerable component.
Confidentiality
HighAll data handled by the component can be read.
Integrity
NoneNothing can be modified.
Availability
HighThe component can be taken down completely.

Vector: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:H. Scored by NVD.

Scoring systemScoreSeverityScored by
CVSS 3.18.1HighCNA (github.com)
CVSS 3.18.1HighNVD
CVSS 2.05.5MediumNVD

Official description

Tensorflow is an Open Source Machine Learning Framework. The implementation of shape inference for `ReverseSequence` does not fully validate the value of `batch_dim` and can result in a heap OOB read. There is a check to make sure the value of `batch_dim` does not go over the rank of the input, but there is no check for negative values. Negative dimensions are allowed in some cases to mimic Python's negative indexing (i.e., indexing from the end of the array), however if the value is too negative then the implementation of `Dim` would access elements before the start of an array. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Source: the CVE record as published by its numbering authority, via NVD and CVE.org.

The underlying weakness: Out-of-bounds Read

The product reads data past the end, or before the beginning, of the intended buffer.

How developers prevent this class of flaw

  • Use a language that provides appropriate memory abstractions.

More Out-of-bounds Read vulnerabilities (CWE-125)

Timeline

  1. CVE record published.
  2. CISA published its SSVC assessment (exploitation: poc, automatable: no, technical impact: partial).
  3. Record last changed materially (score, affected products, status or references).

References

Other vulnerabilities in Tensorflow

All Tensorflow vulnerabilities

Recent Out-of-bounds Read vulnerabilities

Spotted an error? Tell us.