CVSS: 9.0EPSS: 0%CPEs: 6EXPL: 1CVE-2020-15206 – Denial of Service in Tensorflow
https://notcve.org/view.php?id=CVE-2020-15206
In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, changing the TensorFlow's `SavedModel` protocol buffer and altering the name of required keys results in segfaults and data corruption while loading the model. This can cause a denial of service in products using `tensorflow-serving` or other inference-as-a-service installments. Fixed were added in commits f760f88b4267d981e13f4b302c437ae800445968 and fcfef195637c6e365577829c4d67681695956e7d (both going into TensorFlow 2.2.0 and 2.3.0 but not yet backported to earlier versions). However, this was not enough, as #41097 reports a different failure mode. The issue is patched in commit adf095206f25471e864a8e63a0f1caef53a0e3a6, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. • http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html https://github.com/tensorflow/tensorflow/commit/adf095206f25471e864a8e63a0f1caef53a0e3a6 https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1 https://github.com/tensorflow/tensorflow/security/advisories/GHSA-w5gh-2wr2-pm6g • CWE-20: Improper Input Validation •
CVSS: 9.0EPSS: 0%CPEs: 6EXPL: 1CVE-2020-15207 – Segfault and data corruption in tensorflow-lite
https://notcve.org/view.php?id=CVE-2020-15207
In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, to mimic Python's indexing with negative values, TFLite uses `ResolveAxis` to convert negative values to positive indices. However, the only check that the converted index is now valid is only present in debug builds. If the `DCHECK` does not trigger, then code execution moves ahead with a negative index. This, in turn, results in accessing data out of bounds which results in segfaults and/or data corruption. The issue is patched in commit 2d88f470dea2671b430884260f3626b1fe99830a, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. • http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html https://github.com/tensorflow/tensorflow/commit/2d88f470dea2671b430884260f3626b1fe99830a https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1 https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q4qf-3fc6-8x34 • CWE-119: Improper Restriction of Operations within the Bounds of a Memory Buffer CWE-787: Out-of-bounds Write •
CVSS: 9.8EPSS: 0%CPEs: 6EXPL: 1CVE-2020-15208 – Data corruption in tensorflow-lite
https://notcve.org/view.php?id=CVE-2020-15208
In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, when determining the common dimension size of two tensors, TFLite uses a `DCHECK` which is no-op outside of debug compilation modes. Since the function always returns the dimension of the first tensor, malicious attackers can craft cases where this is larger than that of the second tensor. In turn, this would result in reads/writes outside of bounds since the interpreter will wrongly assume that there is enough data in both tensors. The issue is patched in commit 8ee24e7949a203d234489f9da2c5bf45a7d5157d, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. En tensorflow-lite versiones anteriores a 1.15.4, 2.0.3, 2.1.2, 2.2.1 y 2.3.1, al determinar el tamaño de dimensión común de dos tensores, TFLite usa un "DCHECK" que no es operativo fuera de los modos de compilación de depuración. • http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html https://github.com/tensorflow/tensorflow/commit/8ee24e7949a203d234489f9da2c5bf45a7d5157d https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1 https://github.com/tensorflow/tensorflow/security/advisories/GHSA-mxjj-953w-2c2v • CWE-125: Out-of-bounds Read CWE-787: Out-of-bounds Write •
CVSS: 5.9EPSS: 0%CPEs: 6EXPL: 1CVE-2020-15209 – Null pointer dereference in tensorflow-lite
https://notcve.org/view.php?id=CVE-2020-15209
In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, a crafted TFLite model can force a node to have as input a tensor backed by a `nullptr` buffer. This can be achieved by changing a buffer index in the flatbuffer serialization to convert a read-only tensor to a read-write one. The runtime assumes that these buffers are written to before a possible read, hence they are initialized with `nullptr`. However, by changing the buffer index for a tensor and implicitly converting that tensor to be a read-write one, as there is nothing in the model that writes to it, we get a null pointer dereference. The issue is patched in commit 0b5662bc, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. • http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html https://github.com/tensorflow/tensorflow/commit/0b5662bc2be13a8c8f044d925d87fb6e56247cd8 https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1 https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qh32-6jjc-qprm • CWE-476: NULL Pointer Dereference •
CVSS: 6.5EPSS: 0%CPEs: 6EXPL: 1CVE-2020-15210 – Segmentation fault in tensorflow-lite
https://notcve.org/view.php?id=CVE-2020-15210
In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, if a TFLite saved model uses the same tensor as both input and output of an operator, then, depending on the operator, we can observe a segmentation fault or just memory corruption. We have patched the issue in d58c96946b and will release patch releases for all versions between 1.15 and 2.3. We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. En tensorflow-lite versiones anteriores a 1.15.4, 2.0.3, 2.1.2, 2.2.1 y 2.3.1, si un modelo guardado de TFLite usa el mismo tensor como entrada y salida de un operador, entonces, dependiendo del operador, podemos observar un fallo de segmentación o solo una corrupción de la memoria. Hemos parcheado el problema en d58c96946b y publicaremos parches para todas las versiones entre 1.15 y 2.3. • http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html https://github.com/tensorflow/tensorflow/commit/d58c96946b2880991d63d1dacacb32f0a4dfa453 https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1 https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x9j7-x98r-r4w2 • CWE-20: Improper Input Validation CWE-787: Out-of-bounds Write •