CVE-2022-35970 json
Segfault in `QuantizedInstanceNorm` in TensorFlow
published 2022-09-16 4 y ago modified 2026-09-08 2 mo ago also BIT-tensorflow-2022-35970 , EUVD-2022-6814 , GHSA-g35r-369w-3fqp , PYSEC-2026-3183 , PYSEC-2026-3326 , PYSEC-2026-995
- cvss
- 5.9
- version 3.1, CNA
- cisa kev
- Not listed
- not in CISA's catalog
- epss
- 0.5%
- EPSS 0.5% chance of exploitation in 30 days, percentile 43%
- public exploits
- 0
- none known
Summary
TensorFlow is an open source platform for machine learning. If `QuantizedInstanceNorm` is given `x_min` or `x_max` 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.
Patch / fix
- pypi tensorflow 2.7.2, 2.8.1, 2.9.1
- pypi tensorflow-cpu 2.7.2, 2.8.1, 2.9.1
- pypi tensorflow-gpu 2.7.2, 2.8.1, 2.9.1
- bitnami tensorflow 2.7.2, 2.8.1, 2.9.1
- google tensorflow 2.7.2, 2.8.1, 2.9.1
CISA SSVC
- Exploitation
- none
- Automatable
- no
- Technical impact
- partial
- Assessed
- 18 mo ago
Exploit prediction
0.51% chance of exploitation in the next 30 days, higher than 43% of all scored CVEs (FIRST EPSS, 2026-11-08).
CVSS scores
| assigned by | version | score | vector |
|---|---|---|---|
| CNA | 3.1 | 5.9 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H |
| GitHub | 3.1 | 5.9 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H |
| NVD | 3.1 | 7.5 | CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H |
| OSV | 3.1 | 7.5 | CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H |
| OSV | 3.1 | 5.9 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H |
Affected packages
| ecosystem | package | affected | fixed in |
|---|---|---|---|
| pypi | tensorflow | <2.7.2 >=2.8.0, <2.8.1 >=2.9.0, <2.9.1 |
2.7.2 2.8.1 2.9.1 |
| pypi | tensorflow-cpu | <2.7.2 >=2.8.0, <2.8.1 >=2.9.0, <2.9.1 |
2.7.2 2.8.1 2.9.1 |
| pypi | tensorflow-gpu | <2.7.2 >=2.8.0, <2.8.1 >=2.9.0, <2.9.1 |
2.7.2 2.8.1 2.9.1 |
| bitnami | tensorflow | >=2.7.0, <2.7.2 >=2.8.0, <2.8.1 >=2.9.0, <2.9.1 |
2.7.2 2.8.1 2.9.1 |
Affected products
- tensorflow tensorflow
Classification
- Weakness
-
- CWE-20 Improper Input Validation
References
-
github.com
/tensorflow/tensorflow/commit/785d67a78a1d533759fcd2f5e8d6ef778de849e0
web, patch, third-party-advisory, advisory
-
github.com
/tensorflow/tensorflow/security/advisories/GHSA-g35r-369w-3fqp
web, patch, third-party-advisory, advisory
-
nvd.nist.gov
/vuln/detail/CVE-2022-35970
advisory, web
-
github.com
/tensorflow/tensorflow
package
-
github.com
/tensorflow/tensorflow/releases/tag/v2.10.0
web
-
pypi.org
/project/tensorflow
package
-
github.com
/advisories/GHSA-g35r-369w-3fqp
advisory
-
pypi.org
/project/tensorflow-cpu
package
-
pypi.org
/project/tensorflow-gpu
package
Sources
- CVE List CVE-2022-35970
- GitHub Advisory GHSA-g35r-369w-3fqp
- NVD CVE-2022-35970
- OSV BIT-tensorflow-2022-35970
- OSV PYSEC-2026-3183
- OSV PYSEC-2026-3326
- OSV PYSEC-2026-995
Fencer Vulns keeps one entry per vulnerability: the CVE id wins, then GHSA, and records that alias each other are merged, except that two CVEs always keep separate entries. The same data is available as JSON.