TensorFlow is an open source platform for machine learning. If QuantizedBiasAdd is given min_input, max_input, min_bias, max_bias tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 785d67a78a1d533759fcd2f5e8d6ef778de849e0. 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-2022-35972
This medium-severity CVE scores 5.9 under NVD CVSS v3. EPSS exploit probability: 0.4%, top 65% 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
- 34.7%
- KEV
- Not listed
Published
September 16, 2022
Last Modified
November 21, 2024
References (4)
- security-advisories@githubhttps://github.com/tensorflow/tensorflow/commit/785d67a78a1d533759fcd2f5e8d6ef778de849e0
- security-advisories@githubhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-4pc4-m9mj-v2r9
- af854a3a-2127-422b-91ae-364da2661108https://github.com/tensorflow/tensorflow/commit/785d67a78a1d533759fcd2f5e8d6ef778de849e0
- af854a3a-2127-422b-91ae-364da2661108https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4pc4-m9mj-v2r9
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 14× in last 7d / 51× 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.
Showing the most recent 100 of 109 total refreshes for this CVE.
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Related CVEs(same CWE)
Frequently asked(5)
What is CVE-2022-35972?
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Dependency Blast Radius
See which npm, PyPI, Go, and Maven packages are affected by CVE-2022-35972
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