https://cve.radiocsirt.org Contains only the most 10 recent entries. http://www.rssboard.org/rss-specification python-feedgen en Fri, 23 Oct 2026 21:52:17 +0000 https://cve.radiocsirt.org/vuln/cve-2022-29212 <p><strong>Affected:</strong> tensorflow</p> <p>TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, certain TFLite models that were created using TFLite model converter would crash when loaded in the TFLite interpreter. The culprit is that during quantization the scale of values could be greater than 1 but code was always assuming sub-unit scaling. Thus, since code was calling `QuantizeMultiplierSmallerThanOneExp`, the `TFLITE_CHECK_LT` assertion would trigger and abort the process. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.</p> <p><strong>Affected:</strong> tensorflow</p> <p>TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, certain TFLite models that were created using TFLite model converter would crash when loaded in the TFLite interpreter. The culprit is that during quantization the scale of values could be greater than 1 but code was always assuming sub-unit scaling. Thus, since code was calling `QuantizeMultiplierSmallerThanOneExp`, the `TFLITE_CHECK_LT` assertion would trigger and abort the process. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.</p> https://cve.radiocsirt.org/vuln/cve-2022-29212 https://cve.radiocsirt.org/vuln/pysec-2026-3141 <p><strong>Affected:</strong> PyPI: tensorflow</p> <p>### Impact Certain TFLite models that were created using TFLite model converter would crash when loaded in the TFLite interpreter. The culprit is that during quantization the scale of values could be greater than 1 but code was always assuming sub-unit scaling.</p> <p>Thus, since code was calling [`QuantizeMultiplierSmallerThanOneExp`](https://github.com/tensorflow/tensorflow/blob/f3b9bf4c3c0597563b289c0512e98d4ce81f886e/tensorflow/lite/kernels/internal/quantization_util.cc#L114-L123), the `TFLITE_CHECK_LT` assertion would trigger and abort the process.</p> <p>### Patches We have patched the issue in GitHub commit [a989426ee1346693cc015792f11d715f6944f2b8](https://github.com/tensorflow/tensorflow/commit/a989426ee1346693cc015792f11d715f6944f2b8).</p> <p>The fix will be included in TensorFlow 2.9.0. We will also cherrypick this commit on TensorFlow 2.8.1, TensorFlow 2.7.2, and TensorFlow 2.6.4, as these are also affected and still in supported range.</p> <p>### For more information Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.</p> <p>### Attribution This vulnerability has been reported externally via a [GitHub issue](https://github.com/tensorflow/tensorflow/issues/43661).</p> <p><strong>Affected:</strong> PyPI: tensorflow</p> <p>### Impact Certain TFLite models that were created using TFLite model converter would crash when loaded in the TFLite interpreter. The culprit is that during quantization the scale of values could be greater than 1 but code was always assuming sub-unit scaling.</p> <p>Thus, since code was calling [`QuantizeMultiplierSmallerThanOneExp`](https://github.com/tensorflow/tensorflow/blob/f3b9bf4c3c0597563b289c0512e98d4ce81f886e/tensorflow/lite/kernels/internal/quantization_util.cc#L114-L123), the `TFLITE_CHECK_LT` assertion would trigger and abort the process.</p> <p>### Patches We have patched the issue in GitHub commit [a989426ee1346693cc015792f11d715f6944f2b8](https://github.com/tensorflow/tensorflow/commit/a989426ee1346693cc015792f11d715f6944f2b8).</p> <p>The fix will be included in TensorFlow 2.9.0. We will also cherrypick this commit on TensorFlow 2.8.1, TensorFlow 2.7.2, and TensorFlow 2.6.4, as these are also affected and still in supported range.</p> <p>### For more information Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.</p> <p>### Attribution This vulnerability has been reported externally via a [GitHub issue](https://github.com/tensorflow/tensorflow/issues/43661).</p> https://cve.radiocsirt.org/vuln/pysec-2026-3141