CVE-2021-29597

Model-processing crash via zero block dimension

Published May 14, 2021 · Updated Aug 3, 2024

Divide-by-zero in Google TensorFlow TFLite allows local users to crash model execution by loading a crafted SpaceToBatchNd model. ResizeOutputTensor uses a zero block_shape element as the divisor for modulo and integer division without first rejecting it. The attacker must cause a local TensorFlow process to load and execute the malicious model; the documented consequence is a model-processing crash.

CVSS severity2.5
Low
EPSS probability0.20%
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 Google TensorFlow TFLite allows local users to crash model execution by loading a crafted SpaceToBatchNd model. ResizeOutputTensor uses a zero block_shape element as the divisor for modulo and integer division without first rejecting it. The attacker must cause a local TensorFlow process to load and execute the malicious model; the documented consequence is a model-processing crash.

The record

CVE
CVE-2021-29597
Published
May 14, 2021
Updated
Aug 3, 2024
Vendor
Google
Product
TensorFlow
Classifications
CWE-369
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

2.5GitHub, Inc.CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L
7.8NIST NVDCVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
4.6NIST NVDAV:L/AC:L/Au:N/C:P/I:P/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 exploitUnconfirmed

Weakness, pattern, technique

CWE-369Divide By Zero

Public exploit references

No public exploit references are available in this record.

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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