CVE-2020-26266
Description
In affected versions of TensorFlow under certain cases a saved model can trigger use of uninitialized values during code execution. This is caused by having tensor buffers be filled with the default value of the type but forgetting to default initialize the quantized floating point types in Eigen. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0.
Predictions
Heuristic predictions, AS-IS, for prioritization only.
Mitigations
No mitigations published for this CVE yet.
The vendor-content worker queues fetches as references arrive (check back in a few minutes). Or — if you've already worked around this in production — publish your fix to the community-verified tier.
✚ Propose a mitigation on Community → Mitigations published via the community go through AI scoring + 2 human reviewers + 7-day silent objection window before landing here withsource_tier=community-verified.
OS impact
Arch Fixed 1 release
| Version | Status | Fixed in |
|---|---|---|
| — | Fixed | 2.4.0-1 |
Debian Fixed 2 releases
| Version | Status | Fixed in |
|---|---|---|
| sid | Fixed | 0 |
| forky | Fixed | 0 |
Package impact
| Ecosystem | Package | Vulnerable | Fixed |
|---|---|---|---|
| PyPI | tensorflow | <1.15.5 | 1.15.5 |
| PyPI | tensorflow | >=2.0.0,<2.0.4 | 2.0.4 |
| PyPI | tensorflow | >=2.1.0,<2.1.3 | 2.1.3 |
| PyPI | tensorflow | >=2.2.0,<2.2.2 | 2.2.2 |
| PyPI | tensorflow | >=2.3.0,<2.3.2 | 2.3.2 |
| PyPI | tensorflow-cpu | <1.15.5 | 1.15.5 |
| PyPI | tensorflow-cpu | >=2.0.0,<2.0.4 | 2.0.4 |
| PyPI | tensorflow-cpu | >=2.1.0,<2.1.3 | 2.1.3 |
| PyPI | tensorflow-cpu | >=2.2.0,<2.2.2 | 2.2.2 |
| PyPI | tensorflow-cpu | >=2.3.0,<2.3.2 | 2.3.2 |
| PyPI | tensorflow-gpu | <1.15.5 | 1.15.5 |
| PyPI | tensorflow-gpu | >=2.0.0,<2.0.4 | 2.0.4 |
| PyPI | tensorflow-gpu | >=2.1.0,<2.1.3 | 2.1.3 |
| PyPI | tensorflow-gpu | >=2.2.0,<2.2.2 | 2.2.2 |
| PyPI | tensorflow-gpu | >=2.3.0,<2.3.2 | 2.3.2 |
| PyPI | tensorflow | <ace0c15a22f7f054abcc1f53eabbcb0a1239a9e2||>=2.3.0,<2.3.2 | ace0c15a22f7f054abcc1f53eabbcb0a1239a9e2 |
| PyPI | tensorflow-cpu | <ace0c15a22f7f054abcc1f53eabbcb0a1239a9e2||>=2.3.0,<2.3.2 | ace0c15a22f7f054abcc1f53eabbcb0a1239a9e2 |
| PyPI | tensorflow-gpu | <ace0c15a22f7f054abcc1f53eabbcb0a1239a9e2||>=2.3.0,<2.3.2 | ace0c15a22f7f054abcc1f53eabbcb0a1239a9e2 |
References
- https://security.archlinux.org/ASA-202012-22
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qhxx-j73r-qpm2
- https://nvd.nist.gov/vuln/detail/CVE-2020-26266
- https://github.com/tensorflow/tensorflow/commit/ace0c15a22f7f054abcc1f53eabbcb0a1239a9e2
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2020-297.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2020-332.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2020-254.yaml
- https://github.com/tensorflow/tensorflow
- https://security-tracker.debian.org/tracker/CVE-2020-26266
Community-verified mitigations for this CVE will appear above when contributors publish them.
Verify integrity in audit chain (admin only). AS-IS.