CVE-2021-29522Public exploit

Process crash via zero-sized convolution tensors

Published May 14, 2021 · Updated Aug 3, 2024

Divide-by-zero in TensorFlow Conv3D backpropagation allows local users controlling input sizes to crash a consuming process. The kernel computes a shard size by dividing by a work-unit size derived from empty input, filter, and backpropagation tensors without first rejecting zero. Affected releases are before 2.1.4, 2.2.0 through 2.2.2, 2.3.0 through 2.3.2, and 2.4.0 through 2.4.1; reaching the flaw requires control of tensor dimensions, and the impact is limited to termination of the executing process.

CVSS severity2.5
Low
EPSS probability0.19%
Next 30 days · Sep 16, 2026
Known exploitationUnconfirmed
Based on sourced intelligence
Hinoki checkNot available
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Summary

What happened

Divide-by-zero in TensorFlow Conv3D backpropagation allows local users controlling input sizes to crash a consuming process. The kernel computes a shard size by dividing by a work-unit size derived from empty input, filter, and backpropagation tensors without first rejecting zero. Affected releases are before 2.1.4, 2.2.0 through 2.2.2, 2.3.0 through 2.3.2, and 2.4.0 through 2.4.1; reaching the flaw requires control of tensor dimensions, and the impact is limited to termination of the executing process.

The record

CVE
CVE-2021-29522
Published
May 14, 2021
Updated
Aug 3, 2024
Vendor
Google
Product
TensorFlow
Classifications
CWE-369, T1499.004
Attack vector
local
Privileges
authenticated

Timeline

How it unfolded

  1. May 14, 2021CVE publishedPublication date reported by the CVE source.
  2. Aug 3, 2024Record updatedLatest update available in the CVE record.

Exploitability

Present is not the same as exploitable

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Is a vulnerable build present?

Compare these published version ranges with your installed build and any vendor patches.

  1. Affected versionversion=< 2.1.4
  2. Affected versionversion=>= 2.2.0, < 2.2.3
  3. Affected versionversion=>= 2.3.0, < 2.3.3
  4. Affected versionversion=>= 2.4.0, < 2.4.2

What conditions does exploitation require?

Attack vectorlocal
Required privilegesauthenticated

What is affected?

Google · TensorFlowversion=< 2.1.4; version=>= 2.2.0, < 2.2.3; version=>= 2.3.0, < 2.3.3; version=>= 2.4.0, < 2.4.2

Published CVSS scores

5.5NIST NVDCVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
2.5GitHub, Inc.CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L
2.1NIST NVDAV:L/AC:L/Au:N/C:N/I:N/A:P

CVSS describes severity. EPSS estimates exploitation probability.

Attacks

What attackers are doing with it

Daily unique IPs observed by Shadowserver honeypots for known exploited vulnerabilities (KEVs). Missing observations do not establish an absence of attacks.

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No observations available

Sep 10, 2026Sep 16, 2026
Latest reporting daySep 16, 2026
Latest daily unique IPsUnavailable
Prior 30-day averageUnavailable
SourceShadowserver honeypots (KEV)
Vectorlocal
Privilegesauthenticated
Known exploitationUnconfirmed
Public exploitPublished

Weakness, pattern, technique

CWE-369Divide By Zero
T1499.004Application or System Exploitation

Public exploit references

Labels summarize the accepted research assessment. They do not indicate a test against your environment.

Technologies

Your stack

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Your stack

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