TensorFlow 2.10/2.11 Crash via tf.keras.losses.poisson int32 overflow
CVE-2022-41887 Published on November 18, 2022
Overflow in `tf.keras.losses.poisson` in Tensorflow
TensorFlow is an open source platform for machine learning. `tf.keras.losses.poisson` receives a `y_pred` and `y_true` that are passed through `functor::mul` in `BinaryOp`. If the resulting dimensions overflow an `int32`, TensorFlow will crash due to a size mismatch during broadcast assignment. We have patched the issue in GitHub commit c5b30379ba87cbe774b08ac50c1f6d36df4ebb7c. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1 and 2.9.3, as these are also affected and still in supported range. However, we will not cherrypick this commit into TensorFlow 2.8.x, as it depends on Eigen behavior that changed between 2.8 and 2.9.
Vulnerability Analysis
CVE-2022-41887 can be exploited with network access, requires user interaction and a small amount of user privileges. This vulnerability is consided to have a high level of attack complexity. Public availability of a proof of concept (POC) exploit exists for CVE-2022-41887. The potential impact of an exploit of this vulnerability is considered to have no impact on confidentiality and integrity, and a high impact on availability.
Weakness Type
Incorrect Calculation of Buffer Size
The software does not correctly calculate the size to be used when allocating a buffer, which could lead to a buffer overflow.
Products Associated with CVE-2022-41887
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Affected Versions
tensorflow:- Version >= 2.10.0, < 2.10.1 is affected.
- Version < 2.9.3 is affected.
Vulnerable Packages
The following package name and versions may be associated with CVE-2022-41887
| Package Manager | Vulnerable Package | Versions | Fixed In |
|---|---|---|---|
| pip | tensorflow | < 2.9.3 | 2.9.3 |
| pip | tensorflow | >= 2.10.0, < 2.10.1 | 2.10.1 |
| pip | tensorflow-cpu | < 2.9.3 | 2.9.3 |
| pip | tensorflow-gpu | < 2.9.3 | 2.9.3 |
| pip | tensorflow-cpu | >= 2.10.0, < 2.10.1 | 2.10.1 |
| pip | tensorflow-gpu | >= 2.10.0, < 2.10.1 | 2.10.1 |
Exploit Probability
EPSS (Exploit Prediction Scoring System) scores estimate the probability that a vulnerability will be exploited in the wild within the next 30 days. The percentile shows you how this score compares to all other vulnerabilities.