7.8

CVE-2021-37679: google tensorflow Out-of-bounds Read

CVSS: 7.8v3.1HighModified
AliasesGHSA-g8wg-cjwc-xhhpBIT-tensorflow-2021-37679PYSEC-2021-301
Published 12-08-2021Last modified 04-08-2024

Vulnerability summary

CVE-2021-37679 is a high (CVSS 7.8) Out-of-bounds Read vulnerability in google tensorflow. EPSS estimates a 0.18% chance of exploitation in the next 30 days, higher than 7.1% of scored vulnerabilities.

Overall risk
31/100
0100
Medium · default modelHow is this calculated?
CVSS score
7.8/10
010
High · v3.1 (NVD)
EPSS score
0.18%
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

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 it is possible to nest a `tf.map_fn` within another `tf.map_fn` call. However, if the input tensor is a `RaggedTensor` and there is no function signature provided, code assumes the output is a fully specified tensor and fills output buffer with uninitialized contents from the heap. The `t` and `z` outputs should be identical, however this is not the case. The last row of `t` contains data from the heap which can be used to leak other memory information. The bug lies in the conversion from a `Variant` tensor to a `RaggedTensor`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/ragged_tensor_from_variant_op.cc#L177-L190) does not check that all inner shapes match and this results in the additional dimensions. The same implementation can result in data loss, if input tensor is tweaked. We have patched the issue in GitHub commit 4e2565483d0ffcadc719bd44893fb7f609bb5f12. 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

0.18%

Chance of exploitation in the next 30 days

Percentile

7%

Versus all other CVEs

30-day change

0.00 pp

From 0.18% on 18-09-2026

18-09-202618-10-2026
EPSS score
0.18%

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 | < 4e2565483d0ffcadc719bd44893fb7f609bb5f12 | ≥ 2.4.0, < 2.4.3
  • tensorflow-cpu< 2.3.4 | ≥ 2.5.0, < 2.5.1 | < 4e2565483d0ffcadc719bd44893fb7f609bb5f12 | ≥ 2.4.0, < 2.4.3
  • tensorflow-gpu< 2.3.4 | ≥ 2.5.0, < 2.5.1 | < 4e2565483d0ffcadc719bd44893fb7f609bb5f12 | ≥ 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