Process crash via zero-element convolution filter
Published May 14, 2021 · Updated Aug 3, 2024
Divide-by-zero in affected Google TensorFlow releases allows local users to crash a process via a zero-element convolution filter. QuantizedConv2D multiplies the caller-controlled filter width, height, and input depth, then divides its chunk-size limit by that unchecked product. Reaching the fault requires local code to invoke the operation with an empty filter, which can terminate the process hosting that computation.
Summary
What happened
Divide-by-zero in affected Google TensorFlow releases allows local users to crash a process via a zero-element convolution filter. QuantizedConv2D multiplies the caller-controlled filter width, height, and input depth, then divides its chunk-size limit by that unchecked product. Reaching the fault requires local code to invoke the operation with an empty filter, which can terminate the process hosting that computation.
The record
- CVE
- CVE-2021-29527
- Published
- May 14, 2021
- Updated
- Aug 3, 2024
- Vendor
- Product
- TensorFlow
- Classifications
- CWE-369, T1499.004
- Attack vector
- local
- Privileges
- authenticated
Timeline
How it unfolded
- May 14, 2021CVE publishedPublication date reported by the CVE source.
- 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.
- Affected versionversion=< 2.1.4
- Affected versionversion=>= 2.2.0, < 2.2.3
- Affected versionversion=>= 2.3.0, < 2.3.3
- Affected versionversion=>= 2.4.0, < 2.4.2
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
- TensorFlow QuantizedConv2D 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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