TensorFlow is an open source platform for machine learning. The implementation of FractionalAvgPoolGrad does not fully validate the input orig_input_tensor_shape. This results in an overflow that results in a CHECK failure which can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 03a659d7be9a1154fdf5eeac221e5950fec07dad. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.
CVE-2022-35963
This medium-severity CVE scores 5.9 under NVD CVSS v3. EPSS exploit probability: 0.4%, top 66% of all CVEs by exploit prediction. GitHub Security Advisory data not yet ingested — confidence will rise once GHSA publishes (typical lag: hours to days for open-source ecosystem CVEs; never for infrastructure-only CVEs).
- Lower severity and no public exploit yet
No vendor fix yet — apply a workaround or compensating control (WAF / firewall / segmentation) and watch for a patch.
- CVSS v3
- 5.9
- EG Score
- 5.9(medium)
- EG Risk
- 27(Track)EG Risk 27/100SSVC: Track
EG Risk is EchelonGraph's 0–100 priority score: it fuses intrinsic severity with real-world exploitation and automatability so you can rank equal-severity CVEs and fix the most dangerous first. Higher = act sooner. Distinct from the 0–10 EG Score (severity).
How it’s computedSeverity59% × 45%Exploitation0% × 40%Automatability0% × 15%Action: Routine — remediate on your standard cadence. - EPSS
- 33.6%
- KEV
- Not listed
Published
September 16, 2022
Last Modified
November 21, 2024
References (4)
- security-advisories@githubhttps://github.com/tensorflow/tensorflow/commit/03a659d7be9a1154fdf5eeac221e5950fec07dad
- security-advisories@githubhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-84jm-4cf3-9jfm
- af854a3a-2127-422b-91ae-364da2661108https://github.com/tensorflow/tensorflow/commit/03a659d7be9a1154fdf5eeac221e5950fec07dad
- af854a3a-2127-422b-91ae-364da2661108https://github.com/tensorflow/tensorflow/security/advisories/GHSA-84jm-4cf3-9jfm
Affected Packages
(3 across 1 ecosystem)
PyPI(3)
| Package | Vulnerable range | Fixed in | Dependents |
|---|---|---|---|
| tensorflow | 0.12.0 ... 2.9.0 (74 versions) | 2.9.1 | — |
| tensorflow-cpu | 1.15.0 ... 2.9.0 (34 versions) | 2.9.1 | — |
| tensorflow-gpu | 0.12.0 ... 2.9.0 (71 versions) | 2.9.1 | — |
Weakness Classification(1)
MITRE Common Weakness Enumeration — the root-cause categories this CVE belongs to.
Data Freshness Timeline
(refreshed 15× in last 7d / 52× in last 30d)
Each row is a source pipeline that fetched or updated this CVE on that date, with what changed. For example, "NVD update" means NVD published or revised its analysis for this CVE; "MITRE cvelistV5" means we ingested or refreshed it from the CNA feed. Most recent first.
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Related CVEs(same CWE)
Same CWE
10 shownCWE-617
Frequently asked(5)
What is CVE-2022-35963?
When was CVE-2022-35963 disclosed?
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What is the CVSS score of CVE-2022-35963?
How do I remediate CVE-2022-35963?
Dependency Blast Radius
See which npm, PyPI, Go, and Maven packages are affected by CVE-2022-35963
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