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.
Affected products
| Vendor | Product | Affected | Fixed |
|---|---|---|---|
| tensorflow | tensorflow | < 1.15.5, >= 2.0.0, < 2.0.4, >= 2.1.0, < 2.1.3, >= 2.2.0, < 2.2.2, >= 2.3.0, < 2.3.2 | — |
Fixed in Linux distributions
Distributions backport fixes, so the version that fixes this vulnerability differs per release — upgrade to at least the version shown. From the distributions' security trackers and advisories.
SUSE
Security tracker ↗| Release | Package | Fixed in | Advisory |
|---|---|---|---|
| SUSE Package Hub 15 SP3 | bazel-skylib1.0.3 | 1.0.3-bp153.2.1 | openSUSE-SU-2022:10014-1 |
| SUSE Package Hub 15 SP3 | bazel3.7 | 3.7.2-bp153.4.1 | openSUSE-SU-2022:10014-1 |
| SUSE Package Hub 15 SP3 | tensorflow2 +3 | 2.6.0-bp153.2.3.1 | openSUSE-SU-2022:10014-1 |
openSUSE
Security tracker ↗| Release | Package | Fixed in | Advisory |
|---|---|---|---|
| openSUSE Leap 15.3 | bazel-skylib1.0.3 | 1.0.3-bp153.2.1 | openSUSE-SU-2022:10014-1 |
| openSUSE Leap 15.3 | bazel3.7 | 3.7.2-bp153.4.1 | openSUSE-SU-2022:10014-1 |
| openSUSE Leap 15.3 | tensorflow2 +3 | 2.6.0-bp153.2.3.1 | openSUSE-SU-2022:10014-1 |
Affected packages
| Ecosystem | Package | Affected | Fixed | Record |
|---|---|---|---|---|
| PyPI | tensorflow | < 1.15.5 | 1.15.5 | GHSA-qhxx-j73r-qpm2 |
| PyPI | tensorflow | >= 2.0.0, < 2.0.4 | 2.0.4 | GHSA-qhxx-j73r-qpm2 |
| PyPI | tensorflow | >= 2.1.0, < 2.1.3 | 2.1.3 | GHSA-qhxx-j73r-qpm2 |
| PyPI | tensorflow | >= 2.2.0, < 2.2.2 | 2.2.2 | GHSA-qhxx-j73r-qpm2 |
| PyPI | tensorflow | >= 2.3.0, < 2.3.2 | 2.3.2 | GHSA-qhxx-j73r-qpm2 |
| PyPI | tensorflow | < 1.15.5 · >= 2.0.0, < 2.0.4 · >= 2.1.0, < 2.1.3 · >= 2.2.0, < 2.2.2 · >= 2.3.0, < 2.3.2 | 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2 | PYSEC-2020-254 |
| PyPI | tensorflow-cpu | < 1.15.5 | 1.15.5 | GHSA-qhxx-j73r-qpm2 |
| PyPI | tensorflow-cpu | >= 2.0.0, < 2.0.4 | 2.0.4 | GHSA-qhxx-j73r-qpm2 |
| PyPI | tensorflow-cpu | >= 2.1.0, < 2.1.3 | 2.1.3 | GHSA-qhxx-j73r-qpm2 |
| PyPI | tensorflow-cpu | >= 2.2.0, < 2.2.2 | 2.2.2 | GHSA-qhxx-j73r-qpm2 |
| PyPI | tensorflow-cpu | >= 2.3.0, < 2.3.2 | 2.3.2 | GHSA-qhxx-j73r-qpm2 |
| PyPI | tensorflow-cpu | < 1.15.5 · >= 2.0.0, < 2.0.4 · >= 2.1.0, < 2.1.3 · >= 2.2.0, < 2.2.2 · >= 2.3.0, < 2.3.2 | 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2 | PYSEC-2020-297 |
| PyPI | tensorflow-gpu | < 1.15.5 | 1.15.5 | GHSA-qhxx-j73r-qpm2 |
| PyPI | tensorflow-gpu | >= 2.0.0, < 2.0.4 | 2.0.4 | GHSA-qhxx-j73r-qpm2 |
| PyPI | tensorflow-gpu | >= 2.1.0, < 2.1.3 | 2.1.3 | GHSA-qhxx-j73r-qpm2 |
| PyPI | tensorflow-gpu | >= 2.2.0, < 2.2.2 | 2.2.2 | GHSA-qhxx-j73r-qpm2 |
| PyPI | tensorflow-gpu | >= 2.3.0, < 2.3.2 | 2.3.2 | GHSA-qhxx-j73r-qpm2 |
| PyPI | tensorflow-gpu | < 1.15.5 · >= 2.0.0, < 2.0.4 · >= 2.1.0, < 2.1.3 · >= 2.2.0, < 2.2.2 · >= 2.3.0, < 2.3.2 | 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2 | PYSEC-2020-332 |
Advisories 1
- openSUSE-SU-2022:10014-1 Security update for tensorflow2 SUSE / openSUSE Security Updates · Feb 4, 2026