| CVE |
Vendors |
Products |
Updated |
CVSS v3.1 |
| 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. |
| TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.ReverseSequence` allows for stack overflow and/or `CHECK`-fail based denial of service. The implementation(https://github.com/tensorflow/tensorflow/blob/5b3b071975e01f0d250c928b2a8f901cd53b90a7/tensorflow/core/kernels/reverse_sequence_op.cc#L114-L118) fails to validate that `seq_dim` and `batch_dim` arguments are valid. Negative values for `seq_dim` can result in stack overflow or `CHECK`-failure, depending on the version of Eigen code used to implement the operation. Similar behavior can be exhibited by invalid values of `batch_dim`. 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. |
| BPF JIT compilers in the Linux kernel through 5.11.12 have incorrect computation of branch displacements, allowing them to execute arbitrary code within the kernel context. This affects arch/x86/net/bpf_jit_comp.c and arch/x86/net/bpf_jit_comp32.c. |
| Multiple buffer overflow vulnerabilities when parsing a specially crafted file in Esri ArcReader, ArcGIS Desktop, ArcGIS Engine 10.8.1 (and earlier) and ArcGIS Pro 2.7 (and earlier) allow an unauthenticated attacker to achieve arbitrary code execution in the context of the current user. |
| In drivers/pci/hotplug/rpadlpar_sysfs.c in the Linux kernel through 5.11.8, the RPA PCI Hotplug driver has a user-tolerable buffer overflow when writing a new device name to the driver from userspace, allowing userspace to write data to the kernel stack frame directly. This occurs because add_slot_store and remove_slot_store mishandle drc_name '\0' termination, aka CID-cc7a0bb058b8. |
| Because of a incorrect escaped exec command in MagpieRSS in 0.72 in the /extlib/Snoopy.class.inc file, it is possible to add a extra command to the curl binary. This creates an issue on the /scripts/magpie_debug.php and /scripts/magpie_simple.php page that if you send a specific https url in the RSS URL field, you are able to execute arbitrary commands. |
