Application crash via empty Conv2D input
Published Sep 16, 2022 · Updated Apr 23, 2025
Denial of service in Conv2D in Google TensorFlow allows remote attackers to crash applications via an empty input tensor. Conv2DOp continues into convolution kernels when input.NumElements() is zero, causing division by zero instead of returning a zero-filled output. Exploitation requires an application to execute attacker-controlled TensorFlow operations; the fault can interrupt that hosting process on CPU or GPU.
Summary
What happened
Denial of service in Conv2D in Google TensorFlow allows remote attackers to crash applications via an empty input tensor. Conv2DOp continues into convolution kernels when input.NumElements() is zero, causing division by zero instead of returning a zero-filled output. Exploitation requires an application to execute attacker-controlled TensorFlow operations; the fault can interrupt that hosting process on CPU or GPU.
The record
- CVE
- CVE-2022-35996
- Published
- Sep 16, 2022
- Updated
- Apr 23, 2025
- Vendor
- Product
- TensorFlow
- Classifications
- CWE-369, T1499
- Attack vector
- network
- Privileges
- unauthenticated
Timeline
How it unfolded
- Sep 16, 2022CVE publishedPublication date reported by the CVE source.
- Apr 23, 2025Record 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.
- Affected versionversion=< 2.7.2
- Affected versionversion=>= 2.8.0, < 2.8.1
- Affected versionversion=>= 2.9.0, < 2.9.1
What conditions does exploitation require?
What is affected?
Published CVSS scores
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.
Public exploit references
- Empty-input Conv2D denial-of-service proof of conceptproof of concept · demonstrated
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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