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 •
CVSS: 4.3EPSS: 0%CPEs: 2EXPL: 1CVE-2020-15213 – Denial of service in tensorflow-lite
https://notcve.org/view.php?id=CVE-2020-15213
In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation. 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 limit the maximum value in the segment ids tensor. This only handles the case when the segment ids are stored statically in the model, but a similar validation could be done if the segment ids are generated at runtime, between inference steps. • 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-hjmq-236j-8m87 • CWE-119: Improper Restriction of Operations within the Bounds of a Memory Buffer CWE-770: Allocation of Resources Without Limits or Throttling •
CVSS: 8.1EPSS: 0%CPEs: 2EXPL: 1CVE-2020-15214 – Out of bounds write in tensorflow-lite
https://notcve.org/view.php?id=CVE-2020-15214
In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a write out bounds / segmentation fault if the segment ids are not sorted. Code assumes that the segment ids are in increasing order, using the last element of the tensor holding them to determine the dimensionality of output tensor. This results in allocating insufficient memory for the output tensor and in a write outside the bounds of the output array. This usually results in a segmentation fault, but depending on runtime conditions it can provide for a write gadget to be used in future memory corruption-based exploits. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. • 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-p2cq-cprg-frvm • CWE-787: Out-of-bounds Write •