| CVE |
Vendors |
Products |
Updated |
CVSS v3.1 |
| An issue was discovered in Squid before 4.15 and 5.x before 5.0.6. Due to a memory-management bug, it is vulnerable to a Denial of Service attack (against all clients using the proxy) via HTTP Range request processing. |
| In ASQ in Stormshield Network Security (SNS) 1.0.0 through 2.7.8, 2.8.0 through 2.16.0, 3.0.0 through 3.7.20, 3.8.0 through 3.11.8, and 4.0.1 through 4.2.2, mishandling of memory management can lead to remote code execution. |
| This vulnerability allows remote attackers to execute arbitrary code on affected installations of OpenText Brava! Desktop 16.6.3.84. User interaction is required to exploit this vulnerability in that the target must visit a malicious page or open a malicious file. The specific flaw exists within the parsing of DXF files. The issue results from the lack of proper validation of user-supplied data, which can result in a memory corruption condition. An attacker can leverage this vulnerability to execute code in the context of the current process. Was ZDI-CAN-13307. |
| This vulnerability allows remote attackers to execute arbitrary code on affected installations of OpenText Brava! Desktop 16.6.3.84. User interaction is required to exploit this vulnerability in that the target must visit a malicious page or open a malicious file. The specific flaw exists within the parsing of DXF files. The issue results from the lack of proper validation of user-supplied data, which can result in a memory corruption condition. An attacker can leverage this vulnerability to execute code in the context of the current process. Was ZDI-CAN-13304. |
| This vulnerability allows remote attackers to execute arbitrary code on affected installations of Foxit Reader 10.1.1.37576. User interaction is required to exploit this vulnerability in that the target must visit a malicious page or open a malicious file. The specific flaw exists within the handling of U3D objects in PDF files. The issue results from the lack of proper validation of user-supplied data, which can result in a write past the end of an allocated data structure. An attacker can leverage this vulnerability to execute code in the context of the current process. Was ZDI-CAN-13011. |
| The gf_hinter_track_new function in GPAC 1.0.1 allows attackers to read memory via a crafted file in the MP4Box command. |
| A vulnerability in the JNDI Realm of Apache Tomcat allows an attacker to authenticate using variations of a valid user name and/or to bypass some of the protection provided by the LockOut Realm. This issue affects Apache Tomcat 10.0.0-M1 to 10.0.5; 9.0.0.M1 to 9.0.45; 8.5.0 to 8.5.65. |
| Insufficient validation of untrusted input in Sharing in Google Chrome prior to 92.0.4515.107 allowed a remote attacker to bypass navigation restrictions via a crafted click-to-call link. |
| Out of bounds memory access in WebAudio in Google Chrome prior to 91.0.4472.77 allowed a remote attacker to perform out of bounds memory access via a crafted HTML page. |
| A flaw was found in PoDoFo 0.9.7. A stack-based buffer overflow in PdfEncryptMD5Base::ComputeOwnerKey function in PdfEncrypt.cpp is possible because of a improper check of the keyLength value. |
| An issue was discovered in the outer_cgi crate before 0.2.1 for Rust. A user-provided Read instance receives an uninitialized memory buffer from KeyValueReader. |
| Possible out of bound memory access due to improper boundary check while creating HSYNC fence in Snapdragon Auto, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Wearables |
| Possible buffer overflow due to lack of range check while processing a DIAG command for COEX management in Snapdragon Auto, Snapdragon Compute, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wearables |
| Firefox incorrectly treated an inline list-item element as a block element, resulting in an out of bounds read or memory corruption, and a potentially exploitable crash. This vulnerability affects Thunderbird < 78.13, Thunderbird < 91, Firefox ESR < 78.13, and Firefox < 91. |
| IBM Cloud Pak for Automation 21.0.1 and 21.0.2 - Business Automation Studio Component is vulnerable to HTTP header injection, caused by improper validation of input by the HOST headers. By sending a specially crafted HTTP request, a remote attacker could exploit this vulnerability to inject HTTP HOST header, which will allow the attacker to conduct various attacks against the vulnerable system, including cross-site scripting, cache poisoning or session hijacking. IBM X-Force ID: 206228. |
| IBM Maximo Asset Management 7.6.1.1 and 7.6.1.2 is vulnerable to HTTP header injection, caused by improper validation of input by the HOST headers. By sending a specially crafted HTTP request, a remote attacker could exploit this vulnerability to inject HTTP HOST header, which will allow the attacker to conduct various attacks against the vulnerable system, including cross-site scripting, cache poisoning or session hijacking. IBM X-Force ID: 205680. |
| TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPoolGrad` is vulnerable to a heap buffer overflow. The implementation(https://github.com/tensorflow/tensorflow/blob/ab1e644b48c82cb71493f4362b4dd38f4577a1cf/tensorflow/core/kernels/maxpooling_op.cc#L194-L203) fails to validate that indices used to access elements of input/output arrays are valid. Whereas accesses to `input_backprop_flat` are guarded by `FastBoundsCheck`, the indexing in `out_backprop_flat` can result in OOB access. 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. The implementation of `tf.raw_ops.FractionalAvgPoolGrad` is vulnerable to a heap buffer overflow. The implementation(https://github.com/tensorflow/tensorflow/blob/dcba796a28364d6d7f003f6fe733d82726dda713/tensorflow/core/kernels/fractional_avg_pool_op.cc#L216) fails to validate that the pooling sequence arguments have enough elements as required by the `out_backprop` tensor shape. 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. The implementation of `tf.raw_ops.AvgPool3DGrad` is vulnerable to a heap buffer overflow. The implementation(https://github.com/tensorflow/tensorflow/blob/d80ffba9702dc19d1fac74fc4b766b3fa1ee976b/tensorflow/core/kernels/pooling_ops_3d.cc#L376-L450) assumes that the `orig_input_shape` and `grad` tensors have similar first and last dimensions but does not check that this assumption is validated. 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. The implementation of `tf.raw_ops.MaxPool3DGradGrad` is vulnerable to a heap buffer overflow. The implementation(https://github.com/tensorflow/tensorflow/blob/596c05a159b6fbb9e39ca10b3f7753b7244fa1e9/tensorflow/core/kernels/pooling_ops_3d.cc#L694-L696) does not check that the initialization of `Pool3dParameters` completes successfully. Since the constructor(https://github.com/tensorflow/tensorflow/blob/596c05a159b6fbb9e39ca10b3f7753b7244fa1e9/tensorflow/core/kernels/pooling_ops_3d.cc#L48-L88) uses `OP_REQUIRES` to validate conditions, the first assertion that fails interrupts the initialization of `params`, making it contain invalid data. In turn, this might cause a heap buffer overflow, depending on default initialized values. 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. |
