CVE-2022-23593 json
Segfault in `simplifyBroadcast` in Tensorflow
published 4y ago modified 2mo ago also BIT-tensorflow-2022-23593 , EUVD-2022-0286 , GHSA-gwcx-jrx4-92w2 , PYSEC-2022-102 , PYSEC-2022-157 , PYSEC-2026-3193
- cvss
- 5.9
- version 3.1, CNA
- cisa kev
- Not listed
- not in CISA's catalog
- epss
- 0.9%
- EPSS 0.9% chance of exploitation in 30 days, percentile 59%
- public exploits
- 0
- none known
Summary
Tensorflow is an Open Source Machine Learning Framework. The `simplifyBroadcast` function in the MLIR-TFRT infrastructure in TensorFlow is vulnerable to a segfault (hence, denial of service), if called with scalar shapes. If all shapes are scalar, then `maxRank` is 0, so we build an empty `SmallVector`. The fix will be included in TensorFlow 2.8.0. This is the only affected version.
Patch / fix
- pypi tensorflow 2.8.0
- pypi tensorflow-cpu 2.8.0
- pypi tensorflow-gpu 2.8.0
- bitnami tensorflow 2.8.0
- google tensorflow 2.8.0
Is CVE-2022-23593 in your dependencies? Fencer shows which of your repositories are affected as soon as an advisory lands, and opens the fix as a pull request.
Check with FencerCISA SSVC
- Exploitation
- Proof of concept is public
- Automatable
- no
- Technical impact
- partial
- Assessed
- last yr.
Exploit prediction
0.87% chance of exploitation in the next 30 days, higher than 59% of all scored CVEs (FIRST EPSS, 2026-11-09).
Exploits and checks
- github.com https://github.com/tensorflow/tensorflow/blob/274df9b02330b790aa8de1cee164b70f72b9b244/tensorflow/compiler/mlir/tfrt/jit/transforms/tf_cpurt_symbolic_shape_optimization.cc#L149-L205 referenced PoC, tagged "exploit" by a source, unverified
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 |
| NVD | 2.0 | 5.0 | AV:N/AC:L/Au:N/C:N/I:N/A:P |
| 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 | >=2.8.0-rc0, <2.8.0 |
2.8.0 |
| pypi | tensorflow-cpu | >=2.8.0-rc0, <2.8.0 >=2.7.0, <2.8.0 |
2.8.0 |
| pypi | tensorflow-gpu | >=2.8.0-rc0, <2.8.0 >=2.7.0, <2.8.0 |
2.8.0 |
| bitnami | tensorflow | >=2.7.0, <2.8.0 |
2.8.0 |
Affected products
- tensorflow tensorflow
Classification
- Weakness
-
- CWE-754 Improper Check for Unusual or Exceptional Conditions
References
-
github.com
/tensorflow/tensorflow/commit/35f0fabb4c178253a964d7aabdbb15c6a398b69a
web, patch, third-party-advisory, advisory
-
github.com
/tensorflow/tensorflow/security/advisories/GHSA-gwcx-jrx4-92w2
web, patch, third-party-advisory, advisory
-
github.com
/tensorflow/tensorflow/blob/274df9b02330b790aa8de1cee164b70f72b9b244/tensorflow/compiler/mlir/tfrt/jit/transfo
web, exploit, third-party-advisory, advisory
-
nvd.nist.gov
/vuln/detail/CVE-2022-23593
advisory, web
-
github.com
/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2022-102.yaml
web
-
github.com
/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2022-157.yaml
web
-
github.com
/tensorflow/tensorflow
web
-
pypi.org
/project/tensorflow
package
-
github.com
/advisories/GHSA-gwcx-jrx4-92w2
advisory
Sources
- CVE List CVE-2022-23593
- GitHub Advisory GHSA-gwcx-jrx4-92w2
- NVD CVE-2022-23593
- OSV BIT-tensorflow-2022-23593
- OSV PYSEC-2022-102
- OSV PYSEC-2022-157
- OSV PYSEC-2026-3193
Fencer Intel 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.