| CVE |
Vendors |
Products |
Updated |
CVSS v3.1 |
| A vulnerability has been identified in SICAM PAS/PQS (All versions < V7.0), SICAM PAS/PQS (All versions >= 7.0 < V8.06). Affected software does not properly validate the input for a certain parameter in the s7ontcp.dll. This could allow an unauthenticated remote attacker to send messages and create a denial of service condition as the application crashes. At the time of assigning the CVE, the affected firmware version of the component has already been superseded by succeeding mainline versions. |
| Transient DOS due to buffer over-read in WLAN firmware while processing PPE threshold. in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer Electronics Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wearables, Snapdragon Wired Infrastructure and Networking |
| Transient DOS due to buffer over-read in WLAN firmware while parsing cipher suite info attributes. in Snapdragon Compute, Snapdragon Connectivity, Snapdragon Mobile, Snapdragon Wired Infrastructure and Networking |
| Information disclosure due to buffer over-read in WLAN firmware while parsing security context info attributes. in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer Electronics Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wearables, Snapdragon Wired Infrastructure and Networking |
| Transient Denial-of-Service in WLAN due to buffer over-read while parsing MDNS frames. in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer Electronics Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon IoT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wearables, Snapdragon Wired Infrastructure and Networking |
| Memory Corruption in modem due to improper length check while copying into memory in Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Voice & Music |
| Memory corruption in graphics due to buffer overflow while validating the user address in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wearables |
| Memory corruption in camera due to buffer copy without checking size of input in Snapdragon Auto, Snapdragon Compute, Snapdragon Consumer IOT, Snapdragon Mobile, Snapdragon Wearables |
| Memory corruption in camera due to improper validation of array index in Snapdragon Auto, Snapdragon Compute, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Wearables |
| Ember ZNet between v7.2.0 and v7.4.0 used software AES-CCM instead of integrated hardware cryptographic accelerators, potentially increasing risk of electromagnetic and differential power analysis sidechannel attacks. |
| Memory corruption in MODEM due to Improper Validation of Array Index while processing GSTK Proactive commands in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon IoT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wearables |
| Information disclosure in video due to buffer over-read while parsing avi files in Snapdragon Auto, Snapdragon Compute, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Wearables |
| Azure RTOS USBX is a USB host, device, and on-the-go (OTG) embedded stack, that is fully integrated with Azure RTOS ThreadX. Prior to version 6.1.12, the USB DFU UPLOAD functionality may be utilized to introduce a buffer overflow resulting in overwrite of memory contents. In particular cases this may allow an attacker to bypass security features or execute arbitrary code. The implementation of `ux_device_class_dfu_control_request` function prevents buffer overflow during handling of DFU UPLOAD command when current state is `UX_SYSTEM_DFU_STATE_DFU_IDLE`. This issue has been patched, please upgrade to version 6.1.12. As a workaround, add the `UPLOAD_LENGTH` check in all possible states. |
| Azure RTOS FileX is a FAT-compatible file system that’s fully integrated with Azure RTOS ThreadX. In versions before 6.2.0, the Fault Tolerant feature of Azure RTOS FileX includes integer under and overflows which may be exploited to achieve buffer overflow and modify memory contents. When a valid log file with correct ID and checksum is detected by the `_fx_fault_tolerant_enable` function an attempt to recover the previous failed write operation is taken by call of `_fx_fault_tolerant_apply_logs`. This function iterates through the log entries and performs required recovery operations. When properly crafted a log including entries of type `FX_FAULT_TOLERANT_DIR_LOG_TYPE` may be utilized to introduce unexpected behavior. This issue has been patched in version 6.2.0. A workaround to fix line 218 in fx_fault_tolerant_apply_logs.c is documented in the GHSA. |
| TensorFlow is an open source platform for machine learning. When the `BaseCandidateSamplerOp` function receives a value in `true_classes` larger than `range_max`, a heap oob read occurs. We have patched the issue in GitHub commit b389f5c944cadfdfe599b3f1e4026e036f30d2d4. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range. |
| TensorFlow is an open source platform for machine learning. When ops that have specified input sizes receive a differing number of inputs, the executor will crash. We have patched the issue in GitHub commit f5381e0e10b5a61344109c1b7c174c68110f7629. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range. |
| TensorFlow is an open source platform for machine learning. The reference kernel of the `CONV_3D_TRANSPOSE` TensorFlow Lite operator wrongly increments the data_ptr when adding the bias to the result. Instead of `data_ptr += num_channels;` it should be `data_ptr += output_num_channels;` as if the number of input channels is different than the number of output channels, the wrong result will be returned and a buffer overflow will occur if num_channels > output_num_channels. An attacker can craft a model with a specific number of input channels. It is then possible to write specific values through the bias of the layer outside the bounds of the buffer. This attack only works if the reference kernel resolver is used in the interpreter. We have patched the issue in GitHub commit 72c0bdcb25305b0b36842d746cc61d72658d2941. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range. |
| TensorFlow is an open source platform for machine learning. If `MirrorPadGrad` is given outsize input `paddings`, TensorFlow will give a heap OOB error. We have patched the issue in GitHub commit 717ca98d8c3bba348ff62281fdf38dcb5ea1ec92. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range. |
| TensorFlow is an open source platform for machine learning. If `ThreadUnsafeUnigramCandidateSampler` is given input `filterbank_channel_count` greater than the allowed max size, TensorFlow will crash. We have patched the issue in GitHub commit 39ec7eaf1428e90c37787e5b3fbd68ebd3c48860. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range. |
| TensorFlow is an open source platform for machine learning. If `FractionMaxPoolGrad` is given outsize inputs `row_pooling_sequence` and `col_pooling_sequence`, TensorFlow will crash. We have patched the issue in GitHub commit d71090c3e5ca325bdf4b02eb236cfb3ee823e927. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range. |
