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12727 CVE
| CVE | Vendors | Products | Updated | CVSS v2 | CVSS v3 |
|---|---|---|---|---|---|
| CVE-2020-15650 | 2 Google, Mozilla | 2 Android, Firefox Esr | 2024-11-21 | 4.3 MEDIUM | 5.5 MEDIUM |
| Given an installed malicious file picker application, an attacker was able to overwrite local files and thus overwrite Firefox settings (but not access the previous profile). *Note: This issue only affected Firefox for Android. Other operating systems are unaffected.*. This vulnerability affects Firefox ESR < 68.11. | |||||
| CVE-2020-15649 | 2 Google, Mozilla | 2 Android, Firefox Esr | 2024-11-21 | 4.3 MEDIUM | 5.5 MEDIUM |
| Given an installed malicious file picker application, an attacker was able to steal and upload local files of their choosing, regardless of the actually files picked. *Note: This issue only affected Firefox for Android. Other operating systems are unaffected.*. This vulnerability affects Firefox ESR < 68.11. | |||||
| CVE-2020-15647 | 2 Google, Mozilla | 2 Android, Firefox | 2024-11-21 | 5.0 MEDIUM | 7.4 HIGH |
| A Content Provider in Firefox for Android allowed local files accessible by the browser to be read by a remote webpage, leading to sensitive data disclosure, including cookies for other origins. This vulnerability affects Firefox for < Android. | |||||
| CVE-2020-15584 | 1 Google | 1 Android | 2024-11-21 | 7.1 HIGH | 5.5 MEDIUM |
| An issue was discovered on Samsung mobile devices with Q(10.0) software. Attackers can trigger an out-of-bounds access and device reset via a 4K wallpaper image because ImageProcessHelper mishandles boundary checks. The Samsung ID is SVE-2020-18056 (July 2020). | |||||
| CVE-2020-15583 | 1 Google | 1 Android | 2024-11-21 | 2.1 LOW | 5.5 MEDIUM |
| An issue was discovered on Samsung mobile devices with O(8.x), P(9.0), and Q(10.0) software. StickerProvider allows directory traversal for access to system files. The Samsung ID is SVE-2020-17665 (July 2020). | |||||
| CVE-2020-15582 | 2 Google, Samsung | 2 Android, Exynos 7885 | 2024-11-21 | 4.3 MEDIUM | 5.5 MEDIUM |
| An issue was discovered on Samsung mobile devices with P(9.0) and Q(10.0) (Exynos 7885 chipsets) software. The Bluetooth Low Energy (BLE) component has a buffer overflow with a resultant deadlock or crash. The Samsung ID is SVE-2020-16870 (July 2020). | |||||
| CVE-2020-15581 | 1 Google | 1 Android | 2024-11-21 | 5.0 MEDIUM | 5.3 MEDIUM |
| An issue was discovered on Samsung mobile devices with O(8.x), P(9.0), and Q(10.0) software. The kernel logging feature allows attackers to discover virtual addresses via vectors involving shared memory. The Samsung ID is SVE-2020-17605 (July 2020). | |||||
| CVE-2020-15580 | 1 Google | 1 Android | 2024-11-21 | 2.1 LOW | 5.5 MEDIUM |
| An issue was discovered on Samsung mobile devices with O(8.x), P(9.0), and Q(10.0) software. Attackers can bypass Factory Reset Protection (FRP) by enrolling a new lock password. The Samsung ID is SVE-2020-17328 (July 2020). | |||||
| CVE-2020-15579 | 1 Google | 1 Android | 2024-11-21 | 5.0 MEDIUM | 7.5 HIGH |
| An issue was discovered on Samsung mobile devices with O(8.x), P(9.0), and Q(10.0) software. Attackers can bypass Factory Reset Protection (FRP) via the KNOX API. The Samsung ID is SVE-2020-17318 (July 2020). | |||||
| CVE-2020-15578 | 1 Google | 1 Android | 2024-11-21 | 2.1 LOW | 5.5 MEDIUM |
| An issue was discovered on Samsung mobile devices with O(8.x) software. FactoryCamera does not properly restrict runtime permissions. The Samsung ID is SVE-2020-17270 (July 2020). | |||||
| CVE-2020-15577 | 1 Google | 1 Android | 2024-11-21 | 2.1 LOW | 5.5 MEDIUM |
| An issue was discovered on Samsung mobile devices with P(9.0) and Q(10.0) software. Cameralyzer allows attackers to write files to the SD card. The Samsung ID is SVE-2020-16830 (July 2020). | |||||
| CVE-2020-15266 | 1 Google | 1 Tensorflow | 2024-11-21 | 5.0 MEDIUM | 3.7 LOW |
| 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. | |||||
| CVE-2020-15265 | 1 Google | 1 Tensorflow | 2024-11-21 | 5.0 MEDIUM | 5.9 MEDIUM |
| 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. TensorFlow nightly packages after this commit will also have the issue resolved. | |||||
| CVE-2020-15214 | 1 Google | 1 Tensorflow | 2024-11-21 | 6.8 MEDIUM | 8.1 HIGH |
| 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. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that the segment ids are sorted, although this only handles the case when the segment ids are stored statically in the model. A similar validation could be done if the segment ids are generated at runtime between inference steps. If the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code. | |||||
| CVE-2020-15213 | 1 Google | 1 Tensorflow | 2024-11-21 | 4.3 MEDIUM | 4.0 MEDIUM |
| 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. However, if the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code. | |||||
| CVE-2020-15212 | 1 Google | 1 Tensorflow | 2024-11-21 | 7.5 HIGH | 8.1 HIGH |
| 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. A similar validation could be done if the segment ids are generated at runtime between inference steps. If the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code. | |||||
| CVE-2020-15211 | 2 Google, Opensuse | 2 Tensorflow, Leap | 2024-11-21 | 5.8 MEDIUM | 4.8 MEDIUM |
| In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 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 only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code. | |||||
| CVE-2020-15210 | 2 Google, Opensuse | 2 Tensorflow, Leap | 2024-11-21 | 5.8 MEDIUM | 6.5 MEDIUM |
| 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. | |||||
| CVE-2020-15209 | 2 Google, Opensuse | 2 Tensorflow, Leap | 2024-11-21 | 4.3 MEDIUM | 5.9 MEDIUM |
| 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. | |||||
| CVE-2020-15208 | 2 Google, Opensuse | 2 Tensorflow, Leap | 2024-11-21 | 7.5 HIGH | 7.4 HIGH |
| 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. | |||||