CVE-2021-29556Public exploit

Process crash via zero-sized Reverse tensor

Published May 14, 2021 · Updated Aug 3, 2024

Divide-by-zero in Google TensorFlow before its supported branch fixes allows local users to crash a process through tf.raw_ops.Reverse. ReverseOp divides input.NumElements() by input.dim_size(0) without rejecting a tensor whose first dimension is zero. A local user who can supply tensor arguments to a TensorFlow process can trigger a floating-point exception that terminates that process, without reported data disclosure or modification.

CVSS severity5.5
Medium
EPSS probability0.19%
Next 30 days · Sep 16, 2026
Known exploitationUnconfirmed
Based on sourced intelligence
Hinoki checkNot available
Coverage for this vulnerability

See if you're affected

Explore vulnerability checks for your environment with Hinoki.

Book a demo

Summary

What happened

Divide-by-zero in Google TensorFlow before its supported branch fixes allows local users to crash a process through tf.raw_ops.Reverse. ReverseOp divides input.NumElements() by input.dim_size(0) without rejecting a tensor whose first dimension is zero. A local user who can supply tensor arguments to a TensorFlow process can trigger a floating-point exception that terminates that process, without reported data disclosure or modification.

The record

CVE
CVE-2021-29556
Published
May 14, 2021
Updated
Aug 3, 2024
Vendor
Google
Product
TensorFlow
Classifications
CWE-369, T1499
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

Compare your product and version with the public record. A matching version still requires validation against your environment.

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.

Daily unique IPsNo honeypot observations are available for this CVE in the selected window.

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
T1499Endpoint Denial of Service

Public exploit references

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

Technologies

Your stack

See the directory against your own environment.

Your stack

Check the software in your environment

Book a demo to see how Hinoki identifies affected software and validates exploitability in your environment.

Book a demo