| CVE |
Vendors |
Products |
Updated |
CVSS v3.1 |
| An issue was discovered in the Linux kernel before 5.7. The KVM subsystem allows out-of-range access to memslots after a deletion, aka CID-0774a964ef56. This affects arch/s390/kvm/kvm-s390.c, include/linux/kvm_host.h, and virt/kvm/kvm_main.c. |
| Nordic Semiconductor nRF52840 devices through 2020-10-19 have improper protection against physical side channels. The flash read-out protection (APPROTECT) can be bypassed by injecting a fault during the boot phase. |
| Inspur NF5266M5 through 3.21.2 and other server M5 devices allow remote code execution via administrator privileges. The Baseboard Management Controller (BMC) program of INSPUR server is weak in checking the firmware and lacks the signature verification mechanism, the attacker who obtains the administrator's rights can control the BMC by inserting malicious code into the firmware program and bypassing the current verification mechanism to upgrade the BMC. |
| The administration console of the Moog EXO Series EXVF5C-2 and EXVP7C2-3 units features a 'statusbroadcast' command that can spawn a given process repeatedly at a certain time interval as 'root'. One of the limitations of this feature is that it only takes a path to a binary without arguments; however, this can be circumvented using special shell variables, such as '${IFS}'. As a result, an attacker can execute arbitrary commands as 'root' on the units. |
| Moog EXO Series EXVF5C-2 and EXVP7C2-3 units have a hardcoded credentials vulnerability. This could cause a confidentiality issue when using the FTP, Telnet, or SSH protocols. |
| Several XML External Entity (XXE) vulnerabilities in the Moog EXO Series EXVF5C-2 and EXVP7C2-3 units allow remote unauthenticated users to read arbitrary files via a crafted Document Type Definition (DTD) in an XML request. |
| The Moog EXO Series EXVF5C-2 and EXVP7C2-3 units support the ONVIF interoperability IP-based physical security protocol, which requires authentication for some of its operations. It was found that the authentication check for those ONVIF operations can be bypassed. An attacker can abuse this issue to execute privileged operations without authentication, for instance, to create a new Administrator user. |
| Ethereum 0xe933c0cd9784414d5f278c114904f5a84b396919#code.sol latest version is affected by a denial of service vulnerability in the affected payout function. Once the length of this array is too long, it will result in an exception. Attackers can make attacks by creating a series of account addresses. |
| A logic vulnerability when handling the SaveGeneratorLong instruction in Facebook Hermes prior to commit b2021df620824627f5a8c96615edbd1eb7fdddfc allows attackers to potentially read out of bounds or theoretically execute arbitrary code via crafted JavaScript. Note that this is only exploitable if the application using Hermes permits evaluation of untrusted JavaScript. Hence, most React Native applications are not affected. |
| Buffer Overflow vulnerability in function H5S_close in H5S.c in HDF5 1.10.4 allows remote attackers to run arbitrary code via creation of crafted file. |
| Buffer Overflow vulnerability in function H5S_close in H5S.c in HDF5 1.10.4 allows remote attackers to run arbitrary code via creation of crafted file. |
| 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. |
| In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, changing the TensorFlow's `SavedModel` protocol buffer and altering the name of required keys results in segfaults and data corruption while loading the model. This can cause a denial of service in products using `tensorflow-serving` or other inference-as-a-service installments. Fixed were added in commits f760f88b4267d981e13f4b302c437ae800445968 and fcfef195637c6e365577829c4d67681695956e7d (both going into TensorFlow 2.2.0 and 2.3.0 but not yet backported to earlier versions). However, this was not enough, as #41097 reports a different failure mode. The issue is patched in commit adf095206f25471e864a8e63a0f1caef53a0e3a6, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. |
| In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the `Shard` API in TensorFlow expects the last argument to be a function taking two `int64` (i.e., `long long`) arguments. However, there are several places in TensorFlow where a lambda taking `int` or `int32` arguments is being used. In these cases, if the amount of work to be parallelized is large enough, integer truncation occurs. Depending on how the two arguments of the lambda are used, this can result in segfaults, read/write outside of heap allocated arrays, stack overflows, or data corruption. The issue is patched in commits 27b417360cbd671ef55915e4bb6bb06af8b8a832 and ca8c013b5e97b1373b3bb1c97ea655e69f31a575, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. |
| An issue was discovered in HDF5 through 1.12.0. A NULL pointer dereference exists in the function H5F_get_nrefs() located in H5Fquery.c. It allows an attacker to cause Denial of Service. |
| An issue was discovered in HDF5 through 1.12.0. A heap-based buffer over-read exists in the function H5O__layout_decode() located in H5Olayout.c. It allows an attacker to cause Denial of Service. |
| An issue was discovered in HDF5 through 1.12.0. A NULL pointer dereference exists in the function H5AC_unpin_entry() located in H5AC.c. It allows an attacker to cause Denial of Service. |
| An issue was discovered in HDF5 through 1.12.0. A heap-based buffer overflow exists in the function Decompress() located in decompress.c. It can be triggered by sending a crafted file to the gif2h5 binary. It allows an attacker to cause Denial of Service. |
| An issue was discovered in the HDF HDF5 1.10.4 library. There is an out of bounds read in the function H5MM_xstrdup in H5MM.c when called from H5O_dtype_decode_helper in H5Odtype.c. |
| An issue was discovered in the HDF HDF5 1.10.4 library. There is an out of bounds read in the function H5VM_memcpyvv in H5VM.c when called from H5D__compact_readvv in H5Dcompact.c. |
