| CVE |
Vendors |
Products |
Updated |
CVSS v3.1 |
| TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a denial of service via a `CHECK`-fail in `tf.raw_ops.QuantizeAndDequantizeV4Grad`. This is because the implementation does not validate the rank of the `input_*` tensors. In turn, this results in the tensors being passes as they are to `QuantizeAndDequantizePerChannelGradientImpl`. However, the `vec<T>` method, requires the rank to 1 and triggers a `CHECK` failure otherwise. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 as this is the only other affected version. |
| TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a denial of service via a `CHECK`-fail in `tf.raw_ops.SparseConcat`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/b432a38fe0e1b4b904a6c222cbce794c39703e87/tensorflow/core/kernels/sparse_concat_op.cc#L76) takes the values specified in `shapes[0]` as dimensions for the output shape. The `TensorShape` constructor(https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/framework/tensor_shape.cc#L183-L188) uses a `CHECK` operation which triggers when `InitDims`(https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/framework/tensor_shape.cc#L212-L296) returns a non-OK status. This is a legacy implementation of the constructor and operations should use `BuildTensorShapeBase` or `AddDimWithStatus` to prevent `CHECK`-failures in the presence of overflows. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. |
| TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a denial of service via a `CHECK` failure by passing an empty image to `tf.raw_ops.DrawBoundingBoxes`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/ea34a18dc3f5c8d80a40ccca1404f343b5d55f91/tensorflow/core/kernels/image/draw_bounding_box_op.cc#L148-L165) uses `CHECK_*` assertions instead of `OP_REQUIRES` to validate user controlled inputs. Whereas `OP_REQUIRES` allows returning an error condition back to the user, the `CHECK_*` macros result in a crash if the condition is false, similar to `assert`. In this case, `height` is 0 from the `images` input. This results in `max_box_row_clamp` being negative and the assertion being falsified, followed by aborting program execution. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. |
| TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a `CHECK` fail in PNG encoding by providing an empty input tensor as the pixel data. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/kernels/image/encode_png_op.cc#L57-L60) only validates that the total number of pixels in the image does not overflow. Thus, an attacker can send an empty matrix for encoding. However, if the tensor is empty, then the associated buffer is `nullptr`. Hence, when calling `png::WriteImageToBuffer`(https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/kernels/image/encode_png_op.cc#L79-L93), the first argument (i.e., `image.flat<T>().data()`) is `NULL`. This then triggers the `CHECK_NOTNULL` in the first line of `png::WriteImageToBuffer`(https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/lib/png/png_io.cc#L345-L349). Since `image` is null, this results in `abort` being called after printing the stacktrace. Effectively, this allows an attacker to mount a denial of service attack. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. |
| In intel_pmu_drain_pebs_nhm in arch/x86/events/intel/ds.c in the Linux kernel through 5.11.8 on some Haswell CPUs, userspace applications (such as perf-fuzzer) can cause a system crash because the PEBS status in a PEBS record is mishandled, aka CID-d88d05a9e0b6. |
| In the standard library in Rust before 1.52.0, the Zip implementation has a panic safety issue. It calls __iterator_get_unchecked() more than once for the same index when the underlying iterator panics (in certain conditions). This bug could lead to a memory safety violation due to an unmet safety requirement for the TrustedRandomAccess trait. |
| issues with partially successful P2M updates on x86 T[his CNA information record relates to multiple CVEs; the text explains which aspects/vulnerabilities correspond to which CVE.] x86 HVM and PVH guests may be started in populate-on-demand (PoD) mode, to provide a way for them to later easily have more memory assigned. Guests are permitted to control certain P2M aspects of individual pages via hypercalls. These hypercalls may act on ranges of pages specified via page orders (resulting in a power-of-2 number of pages). In some cases the hypervisor carries out the requests by splitting them into smaller chunks. Error handling in certain PoD cases has been insufficient in that in particular partial success of some operations was not properly accounted for. There are two code paths affected - page removal (CVE-2021-28705) and insertion of new pages (CVE-2021-28709). (We provide one patch which combines the fix to both issues.) |
| issues with partially successful P2M updates on x86 T[his CNA information record relates to multiple CVEs; the text explains which aspects/vulnerabilities correspond to which CVE.] x86 HVM and PVH guests may be started in populate-on-demand (PoD) mode, to provide a way for them to later easily have more memory assigned. Guests are permitted to control certain P2M aspects of individual pages via hypercalls. These hypercalls may act on ranges of pages specified via page orders (resulting in a power-of-2 number of pages). In some cases the hypervisor carries out the requests by splitting them into smaller chunks. Error handling in certain PoD cases has been insufficient in that in particular partial success of some operations was not properly accounted for. There are two code paths affected - page removal (CVE-2021-28705) and insertion of new pages (CVE-2021-28709). (We provide one patch which combines the fix to both issues.) |
| An issue was discovered in netplex json-smart-v1 through 2015-10-23 and json-smart-v2 through 2.4. An exception is thrown from a function, but it is not caught, as demonstrated by NumberFormatException. When it is not caught, it may cause programs using the library to crash or expose sensitive information. |
| A maliciously crafted DWG file can be used to write beyond the allocated buffer while parsing DWG files. The vulnerability exists because the application fails to handle a crafted DWG file, which causes an unhandled exception. An attacker can leverage this vulnerability to execute arbitrary code. |
| An issue was discovered in JerryScript 2.4.0. There is a SEGV in main_print_unhandled_exception in main-utils.c file. |
| An issue was discovered in Joomla! 2.5.0 through 3.9.27. Install action in com_installer lack the required hardcoded ACL checks for superusers. A default system is not affected cause the default ACL for com_installer is limited to super users already. |
| A vulnerability has been identified in SIMATIC HMI Comfort Outdoor Panels V15 7\" & 15\" (incl. SIPLUS variants) (All versions < V15.1 Update 6), SIMATIC HMI Comfort Outdoor Panels V16 7\" & 15\" (incl. SIPLUS variants) (All versions < V16 Update 4), SIMATIC HMI Comfort Panels V15 4\" - 22\" (incl. SIPLUS variants) (All versions < V15.1 Update 6), SIMATIC HMI Comfort Panels V16 4\" - 22\" (incl. SIPLUS variants) (All versions < V16 Update 4), SIMATIC HMI KTP Mobile Panels V15 KTP400F, KTP700, KTP700F, KTP900 and KTP900F (All versions < V15.1 Update 6), SIMATIC HMI KTP Mobile Panels V16 KTP400F, KTP700, KTP700F, KTP900 and KTP900F (All versions < V16 Update 4), SIMATIC WinCC Runtime Advanced V15 (All versions < V15.1 Update 6), SIMATIC WinCC Runtime Advanced V16 (All versions < V16 Update 4). SmartVNC client fails to handle an exception properly if the program execution process is modified after sending a packet from the server, which could result in a Denial-of-Service condition. |
| Improper check or handling of exception conditions vulnerability in Samsung Pay (US only) prior to version 4.0.65 allows attacker to use NFC without user recognition. |
| An improper check or handling of exceptional conditions in Exynos baseband prior to SMR Dec-2021 Release 1 allows attackers to track locations. |
| An improper error handling in Exynos CP booting driver prior to SMR Oct-2021 Release 1 allows local attackers to bypass a Secure Memory Protector of Exynos CP Memory. |
| Assuming a shell privilege is gained, an improper exception handling for multi_sim_bar_show_on_qspanel value in SystemUI prior to SMR Oct-2021 Release 1 allows an attacker to cause a permanent denial of service in user device before factory reset. |
| Assuming a shell privilege is gained, an improper exception handling for multi_sim_bar_hide_by_meadia_full value in SystemUI prior to SMR Oct-2021 Release 1 allows an attacker to cause a permanent denial of service in user device before factory reset. |
| Improper check vulnerability in Samsung Health prior to version 6.17 allows attacker to read internal cache data via exported component. |
| Improper handling of exceptional conditions in Bixby prior to version 3.0.53.02 allows attacker to execute the actions registered by the user. |
