CVSS: 4.4EPSS: 0%CPEs: 5EXPL: 0CVE-2020-26270 – CHECK-fail in LSTM with zero-length input in TensorFlow
https://notcve.org/view.php?id=CVE-2020-26270
In affected versions of TensorFlow running an LSTM/GRU model where the LSTM/GRU layer receives an input with zero-length results in a CHECK failure when using the CUDA backend. This can result in a query-of-death vulnerability, via denial of service, if users can control the input to the layer. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0. En las versiones afectadas de TensorFlow que ejecutan un modelo LSTM/GRU donde la capa LSTM/GRU recibe una entrada con longitud cero, se produce un fallo de COMPROBACIÓN cuando se usa el backend CUDA. Esto puede resultar en una vulnerabilidad query-of-death, por medio de la denegación de servicio, si los usuarios pueden controlar la entrada a la capa. • https://github.com/tensorflow/tensorflow/commit/14755416e364f17fb1870882fa778c7fec7f16e3 https://github.com/tensorflow/tensorflow/security/advisories/GHSA-m648-33qf-v3gp • CWE-20: Improper Input Validation •
CVSS: 4.4EPSS: 0%CPEs: 5EXPL: 1CVE-2020-26271 – Heap out of bounds access in MakeEdge in TensorFlow
https://notcve.org/view.php?id=CVE-2020-26271
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. • https://github.com/tensorflow/tensorflow/commit/0cc38aaa4064fd9e79101994ce9872c6d91f816b https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q263-fvxm-m5mw • CWE-125: Out-of-bounds Read CWE-908: Use of Uninitialized Resource •
CVSS: 7.5EPSS: 0%CPEs: 1EXPL: 1CVE-2020-15266 – Undefined behavior in Tensorflow
https://notcve.org/view.php?id=CVE-2020-15266
In Tensorflow before version 2.4.0, when the `boxes` argument of `tf.image.crop_and_resize` has a very large value, the CPU kernel implementation receives it as a C++ `nan` floating point value. Attempting to operate on this is undefined behavior which later produces a segmentation fault. The issue is patched in eccb7ec454e6617738554a255d77f08e60ee0808 and TensorFlow 2.4.0 will be released containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved. En Tensorflow versiones anteriores a 2.4.0, cuando el argumento "boxes" de la función "tf.image.crop_and_resize" presenta un valor muy grande, la implementación del kernel de la CPU lo recibe como un valor de punto flotante "nan" de C++. • https://github.com/tensorflow/tensorflow/issues/42129 https://github.com/tensorflow/tensorflow/pull/42143/commits/3ade2efec2e90c6237de32a19680caaa3ebc2845 https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xwhf-g6j5-j5gc • CWE-119: Improper Restriction of Operations within the Bounds of a Memory Buffer •
CVSS: 7.5EPSS: 0%CPEs: 1EXPL: 1CVE-2020-15265 – Segfault in Tensorflow
https://notcve.org/view.php?id=CVE-2020-15265
In Tensorflow before version 2.4.0, an attacker can pass an invalid `axis` value to `tf.quantization.quantize_and_dequantize`. This results in accessing a dimension outside the rank of the input tensor in the C++ kernel implementation. However, dim_size only does a DCHECK to validate the argument and then uses it to access the corresponding element of an array. Since in normal builds, `DCHECK`-like macros are no-ops, this results in segfault and access out of bounds of the array. The issue is patched in eccb7ec454e6617738554a255d77f08e60ee0808 and TensorFlow 2.4.0 will be released containing the patch. • https://github.com/tensorflow/tensorflow/commit/eccb7ec454e6617738554a255d77f08e60ee0808 https://github.com/tensorflow/tensorflow/issues/42105 https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rrfp-j2mp-hq9c • CWE-125: Out-of-bounds Read •
CVSS: 8.6EPSS: 0%CPEs: 2EXPL: 1CVE-2020-15212 – Out of bounds access in tensorflow-lite
https://notcve.org/view.php?id=CVE-2020-15212
In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor. Users having access to `segment_ids_data` can alter `output_index` and then write to outside of `output_data` buffer. This might result in a segmentation fault but it can also be used to further corrupt the memory and can be chained with other vulnerabilities to create more advanced exploits. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that the segment ids are all positive, although this only handles the case when the segment ids are stored statically in the model. • https://github.com/tensorflow/tensorflow/commit/204945b19e44b57906c9344c0d00120eeeae178a https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1 https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hx2x-85gr-wrpq • CWE-787: Out-of-bounds Write •