CVSS: 5.3EPSS: 0%CPEs: 6EXPL: 1CVE-2020-15204 – Segfault in Tensorflow
https://notcve.org/view.php?id=CVE-2020-15204
In eager mode, TensorFlow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 does not set the session state. Hence, calling `tf.raw_ops.GetSessionHandle` or `tf.raw_ops.GetSessionHandleV2` results in a null pointer dereference In linked snippet, in eager mode, `ctx->session_state()` returns `nullptr`. Since code immediately dereferences this, we get a segmentation fault. The issue is patched in commit 9a133d73ae4b4664d22bd1aa6d654fec13c52ee1, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. En modo eager, TensorFlow versiones anteriores a 1.15.4, 2.0.3, 2.1.2, 2.2.1 y 2.3.1 no establece el estado de la sesión. • http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html https://github.com/tensorflow/tensorflow/commit/9a133d73ae4b4664d22bd1aa6d654fec13c52ee1 https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1 https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q8gv-q7wr-9jf8 • CWE-476: NULL Pointer Dereference •
CVSS: 9.8EPSS: 0%CPEs: 6EXPL: 1CVE-2020-15205 – Data leak in Tensorflow
https://notcve.org/view.php?id=CVE-2020-15205
In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the `data_splits` argument of `tf.raw_ops.StringNGrams` lacks validation. This allows a user to pass values that can cause heap overflow errors and even leak contents of memory In the linked code snippet, all the binary strings after `ee ff` are contents from the memory stack. Since these can contain return addresses, this data leak can be used to defeat ASLR. The issue is patched in commit 0462de5b544ed4731aa2fb23946ac22c01856b80, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. En Tensorflow versiones anteriores a 1.15.4, 2.0.3, 2.1.2, 2.2.1 y 2.3.1, el argumento "data_splits" de "tf.raw_ops.StringNGrams" carece de comprobación. • http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html https://github.com/tensorflow/tensorflow/commit/0462de5b544ed4731aa2fb23946ac22c01856b80 https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1 https://github.com/tensorflow/tensorflow/security/advisories/GHSA-g7p5-5759-qv46 • CWE-119: Improper Restriction of Operations within the Bounds of a Memory Buffer CWE-122: Heap-based Buffer Overflow CWE-787: Out-of-bounds Write •
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 •