7.8

CVE-2021-37663: google tensorflow Improper Input Validation

CVSS: 7.8v3.1HighModified
AliasesGHSA-g25h-jr74-qp5jBIT-tensorflow-2021-37663PYSEC-2021-285
Published 12-08-2021Last modified 04-08-2024
Open JSON

Vulnerability summary

CVE-2021-37663 is a high (CVSS 7.8) Improper Input Validation vulnerability in google tensorflow. EPSS estimates a 0.17% chance of exploitation in the next 30 days, higher than 6.4% of scored vulnerabilities.

Overall risk
31/100
0100
Medium · default modelHow is this calculated?
CVSS score
7.8/10
010
High · v3.1 (CVE.org)
EPSS score
0.17%
0%100%
Low · 0.00 pp in 30 days
KEVNot listedIn no KEV catalog
RansomwareNone recordedNo known use recorded
Public exploits0No public exploits recorded

Advisory lineage

Upstream 0 · Downstream 1 · Related 1
OPENSUSE-SU-2022:10014-1

Timeline

  1. 12-08-2021
    PublishedVulnerability first disclosed.
  2. 04-08-2024
    Last modifiedVulnerability information updated.

Description

TensorFlow is an end-to-end open source platform for machine learning. In affected versions due to incomplete validation in `tf.raw_ops.QuantizeV2`, an attacker can trigger undefined behavior via binding a reference to a null pointer or can access data outside the bounds of heap allocated arrays. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/quantize_op.cc#L59) has some validation but does not check that `min_range` and `max_range` both have the same non-zero number of elements. If `axis` is provided (i.e., not `-1`), then validation should check that it is a value in range for the rank of `input` tensor and then the lengths of `min_range` and `max_range` inputs match the `axis` dimension of the `input` tensor. We have patched the issue in GitHub commit 6da6620efad397c85493b8f8667b821403516708. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.

CVSS metrics

5 scores · 3 sources · 3 versions

EPSS trend

Probability

0.17%

Chance of exploitation in the next 30 days

Percentile

6%

Versus all other CVEs

30-day change

0.00 pp

From 0.17% on 19-09-2026

19-09-202619-10-2026
EPSS score
0.17%

Known exploited

Compare KEV catalogs

Not in any of the 3 tracked catalogs.

Public exploits

0 found

No public exploits indexed.

Weaknesses

CWE · root cause

Affected systems

PyPI3 products
  • tensorflow< 2.3.4 | ≥ 2.5.0, < 2.5.1 | < 6da6620efad397c85493b8f8667b821403516708 | ≥ 2.4.0, < 2.4.3
  • tensorflow-cpu< 2.3.4 | ≥ 2.5.0, < 2.5.1 | < 6da6620efad397c85493b8f8667b821403516708 | ≥ 2.4.0, < 2.4.3
  • tensorflow-gpu< 2.3.4 | ≥ 2.5.0, < 2.5.1 | < 6da6620efad397c85493b8f8667b821403516708 | ≥ 2.4.0, < 2.4.3
Google1 product
  • tensorflow≥ 2.3.0, < 2.3.4 | ≥ 2.4.0, < 2.4.3 | 2.5.0 | 2.6.0:rc0 | 2.6.0:rc1 | 2.6.0:rc2
Tensorflow1 product
  • tensorflow≥ 2.5.0, < 2.5.1 | ≥ 2.4.0, < 2.4.3 | < 2.3.4

3 vendors · 5 products

References

7 links