Denial of service via nonscalar quantization bounds
Published Sep 16, 2022 · Updated Apr 23, 2025
Reachable assertion in Google TensorFlow before 2.7.2 allows remote attackers to cause service interruption via nonscalar min or max inputs. The tf.quantization.fake_quant_with_min_max_vars_gradient operation accepts nonscalar min or max values and reaches a failing CHECK assertion that terminates processing. The attack requires access to a TensorFlow execution interface that processes attacker-controlled tensors; impact is denial of service without reported data disclosure or modification.
Summary
What happened
Reachable assertion in Google TensorFlow before 2.7.2 allows remote attackers to cause service interruption via nonscalar min or max inputs. The tf.quantization.fake_quant_with_min_max_vars_gradient operation accepts nonscalar min or max values and reaches a failing CHECK assertion that terminates processing. The attack requires access to a TensorFlow execution interface that processes attacker-controlled tensors; impact is denial of service without reported data disclosure or modification.
The record
- CVE
- CVE-2022-36005
- Published
- Sep 16, 2022
- Updated
- Apr 23, 2025
- Vendor
- Product
- TensorFlow
- Classifications
- CWE-617, 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
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Attacks
What attackers are doing with it
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Public exploit references
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Technologies
Your stack
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Your stack
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