Memory corruption via unchecked Grappler output index
Published Feb 4, 2022 · Updated Apr 22, 2025
Heap out-of-bounds write in Grappler in Google TensorFlow affected releases allows remote authenticated users to corrupt process memory. SetUnknownShape passes an attacker-controlled output_port to InferenceContext::set_output without validating it against num_outputs, causing an indexed write beyond outputs_. Reachability requires an application to invoke Grappler on an attacker-controlled graph; the primitive can corrupt memory or crash the TensorFlow process.
Summary
What happened
Heap out-of-bounds write in Grappler in Google TensorFlow affected releases allows remote authenticated users to corrupt process memory. SetUnknownShape passes an attacker-controlled output_port to InferenceContext::set_output without validating it against num_outputs, causing an indexed write beyond outputs_. Reachability requires an application to invoke Grappler on an attacker-controlled graph; the primitive can corrupt memory or crash the TensorFlow process.
The record
- CVE
- CVE-2022-23566
- Published
- Feb 4, 2022
- Updated
- Apr 22, 2025
- Vendor
- Product
- TensorFlow
- Classifications
- CWE-787
- Attack vector
- network
- Privileges
- authenticated
Timeline
How it unfolded
- Feb 4, 2022CVE publishedPublication date reported by the CVE source.
- Apr 22, 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?
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- Affected versionversion=< 2.5.3
- Affected versionversion=>= 2.6.0, < 2.6.3
- Affected versionversion=>= 2.7.0, < 2.7.1
What conditions does exploitation require?
What is affected?
Published CVSS scores
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Attacks
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Weakness, pattern, technique
Public exploit references
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Technologies
Your stack
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Your stack
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