Description
In affected versions of TensorFlow under certain cases, loading a saved model can result in accessing uninitialized memory while building the computation graph. The MakeEdge function creates an edge between one output tensor of the src node (given by output_index) and the input slot of the dst node (given by input_index). This is only possible if the types of the tensors on both sides coincide, so the function begins by obtaining the corresponding DataType values and comparing these for equality. However, there is no check that the indices point to inside of the arrays they index into. Thus, this can result in accessing data out of bounds of the corresponding heap allocated arrays. In most scenarios, this can manifest as unitialized data access, but if the index points far away from the boundaries of the arrays this can be used to leak addresses from the library. 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-q263-fvxm-m5mw |
| PyPI | tensorflow | >= 2.0.0, < 2.0.4 | 2.0.4 | GHSA-q263-fvxm-m5mw |
| PyPI | tensorflow | >= 2.1.0, < 2.1.3 | 2.1.3 | GHSA-q263-fvxm-m5mw |
| PyPI | tensorflow | >= 2.2.0, < 2.2.2 | 2.2.2 | GHSA-q263-fvxm-m5mw |
| PyPI | tensorflow | >= 2.3.0, < 2.3.2 | 2.3.2 | GHSA-q263-fvxm-m5mw |
| 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-2020-257 |
| PyPI | tensorflow-cpu | < 1.15.5 | 1.15.5 | GHSA-q263-fvxm-m5mw |
| PyPI | tensorflow-cpu | >= 2.0.0, < 2.0.4 | 2.0.4 | GHSA-q263-fvxm-m5mw |
| PyPI | tensorflow-cpu | >= 2.1.0, < 2.1.3 | 2.1.3 | GHSA-q263-fvxm-m5mw |
| PyPI | tensorflow-cpu | >= 2.2.0, < 2.2.2 | 2.2.2 | GHSA-q263-fvxm-m5mw |
| PyPI | tensorflow-cpu | >= 2.3.0, < 2.3.2 | 2.3.2 | GHSA-q263-fvxm-m5mw |
| 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-2020-302 |
| PyPI | tensorflow-gpu | < 1.15.5 | 1.15.5 | GHSA-q263-fvxm-m5mw |
| PyPI | tensorflow-gpu | >= 2.0.0, < 2.0.4 | 2.0.4 | GHSA-q263-fvxm-m5mw |
| PyPI | tensorflow-gpu | >= 2.1.0, < 2.1.3 | 2.1.3 | GHSA-q263-fvxm-m5mw |
| PyPI | tensorflow-gpu | >= 2.2.0, < 2.2.2 | 2.2.2 | GHSA-q263-fvxm-m5mw |
| PyPI | tensorflow-gpu | >= 2.3.0, < 2.3.2 | 2.3.2 | GHSA-q263-fvxm-m5mw |
| 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-2020-337 |
Advisories 1
- openSUSE-SU-2022:10014-1 Security update for tensorflow2 SUSE / openSUSE Security Updates · Feb 4, 2026