Process memory exhaustion via unchecked negative pad width
Published Feb 3, 2022 · Updated May 5, 2025
Denial of service in TensorFlow 2.5.2 and earlier, 2.6.0 through 2.6.2, and 2.7.0 allows attackers to exhaust process memory. StringNGrams accepts a negative pad_width when ngram_widths is empty and preserve_short_sequences is enabled, producing a negative ngram_width that is used when allocating output. Exploitation requires control of the operation's arguments and can interrupt the hosting process without reported data disclosure or modification.
Summary
What happened
Denial of service in TensorFlow 2.5.2 and earlier, 2.6.0 through 2.6.2, and 2.7.0 allows attackers to exhaust process memory. StringNGrams accepts a negative pad_width when ngram_widths is empty and preserve_short_sequences is enabled, producing a negative ngram_width that is used when allocating output. Exploitation requires control of the operation's arguments and can interrupt the hosting process without reported data disclosure or modification.
The record
- CVE
- CVE-2022-21733
- Published
- Feb 3, 2022
- Updated
- May 5, 2025
- Vendor
- Unknown vendor
- Product
- Unknown product
- Classifications
- CWE-190, T1499
- Attack vector
- network
- Privileges
- authenticated
Timeline
How it unfolded
- Feb 3, 2022CVE publishedPublication date reported by the CVE source.
- May 5, 2025Record updatedLatest update available in the CVE record.
Exploitability
Present is not the same as exploitable
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What is affected?
Affected products and versions are unavailable in this record.
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
- StringNGrams negative pad-width proof of conceptproof of concept · demonstrated
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Technologies
Your stack
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Your stack
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