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111 CVE
| CVE | Vendors | Products | Updated | CVSS v3.1 |
|---|---|---|---|---|
| CVE-2022-40138 | 1 Facebook | 1 Hermes | 2024-11-21 | 9.8 Critical |
| An integer conversion error in Hermes bytecode generation, prior to commit 6aa825e480d48127b480b08d13adf70033237097, could have been used to perform Out-Of-Bounds operations and subsequently execute arbitrary code. Note that this is only exploitable in cases where Hermes is used to execute untrusted JavaScript. Hence, most React Native applications are not affected. | ||||
| CVE-2022-36025 | 1 Linuxfoundation | 1 Besu | 2024-11-21 | 9.1 Critical |
| Besu is a Java-based Ethereum client. In versions newer than 22.1.3 and prior to 22.7.1, Besu is subject to an Incorrect Conversion between Numeric Types. An error in 32 bit signed and unsigned types in the calculation of available gas in the CALL operations (including DELEGATECALL) results in incorrect gas being passed into called contracts and incorrect gas being returned after call execution. Where the amount of gas makes a difference in the success or failure, or if the gas is a negative 64 bit value, the execution will result in a different state root than expected, resulting in a consensus failure in networks with multiple EVM implementations. In networks with a single EVM implementation this can be used to execute with significantly more gas than then transaction requested, possibly exceeding gas limitations. This issue is patched in version 22.7.1. As a workaround, reverting to version 22.1.3 or earlier will prevent incorrect execution. | ||||
| CVE-2022-34680 | 6 Citrix, Debian, Linux and 3 more | 13 Hypervisor, Debian Linux, Linux Kernel and 10 more | 2024-11-21 | 5.5 Medium |
| NVIDIA GPU Display Driver for Linux contains a vulnerability in the kernel mode layer handler, where an integer truncation can lead to an out-of-bounds read, which may lead to denial of service. | ||||
| CVE-2022-34677 | 6 Citrix, Debian, Linux and 3 more | 13 Hypervisor, Debian Linux, Linux Kernel and 10 more | 2024-11-21 | 5.5 Medium |
| NVIDIA GPU Display Driver for Linux contains a vulnerability in the kernel mode layer handler, where an unprivileged regular user can cause an integer to be truncated, which may lead to denial of service or data tampering. | ||||
| CVE-2022-34670 | 6 Citrix, Debian, Linux and 3 more | 13 Hypervisor, Debian Linux, Linux Kernel and 10 more | 2024-11-21 | 7.8 High |
| NVIDIA GPU Display Driver for Linux contains a vulnerability in the kernel mode layer handler, where an unprivileged regular user can cause truncation errors when casting a primitive to a primitive of smaller size causes data to be lost in the conversion, which may lead to denial of service or information disclosure. | ||||
| CVE-2022-34169 | 7 Apache, Azul, Debian and 4 more | 23 Xalan-java, Zulu, Debian Linux and 20 more | 2024-11-21 | 7.5 High |
| The Apache Xalan Java XSLT library is vulnerable to an integer truncation issue when processing malicious XSLT stylesheets. This can be used to corrupt Java class files generated by the internal XSLTC compiler and execute arbitrary Java bytecode. Users are recommended to update to version 2.7.3 or later. Note: Java runtimes (such as OpenJDK) include repackaged copies of Xalan. | ||||
| CVE-2022-32547 | 3 Fedoraproject, Imagemagick, Redhat | 3 Fedora, Imagemagick, Enterprise Linux | 2024-11-21 | 7.8 High |
| In ImageMagick, there is load of misaligned address for type 'double', which requires 8 byte alignment and for type 'float', which requires 4 byte alignment at MagickCore/property.c. Whenever crafted or untrusted input is processed by ImageMagick, this causes a negative impact to application availability or other problems related to undefined behavior. | ||||
| CVE-2022-2639 | 2 Linux, Redhat | 7 Linux Kernel, Enterprise Linux, Rhel Aus and 4 more | 2024-11-21 | 7.8 High |
| An integer coercion error was found in the openvswitch kernel module. Given a sufficiently large number of actions, while copying and reserving memory for a new action of a new flow, the reserve_sfa_size() function does not return -EMSGSIZE as expected, potentially leading to an out-of-bounds write access. This flaw allows a local user to crash or potentially escalate their privileges on the system. | ||||
| CVE-2022-27882 | 1 Openbsd | 1 Openbsd | 2024-11-21 | 7.5 High |
| slaacd in OpenBSD 6.9 and 7.0 before 2022-03-22 has an integer signedness error and resultant heap-based buffer overflow triggerable by a crafted IPv6 router advertisement. NOTE: privilege separation and pledge can prevent exploitation. | ||||
| CVE-2022-27189 | 1 F5 | 11 Big-ip Access Policy Manager, Big-ip Advanced Firewall Manager, Big-ip Analytics and 8 more | 2024-11-21 | 7.5 High |
| On F5 BIG-IP 16.1.x versions prior to 16.1.2.2, 15.1.x versions prior to 15.1.5.1, 14.1.x versions prior to 14.1.4.6, 13.1.x versions prior to 13.1.5, and all versions of 12.1.x and 11.6.x, when an Internet Content Adaptation Protocol (ICAP) profile is configured on a virtual server, undisclosed traffic can cause an increase in Traffic Management Microkernel (TMM) memory resource utilization. Note: Software versions which have reached End of Technical Support (EoTS) are not evaluated | ||||
| CVE-2022-0322 | 4 Fedoraproject, Linux, Oracle and 1 more | 6 Fedora, Linux Kernel, Communications Cloud Native Core Binding Support Function and 3 more | 2024-11-21 | 5.5 Medium |
| A flaw was found in the sctp_make_strreset_req function in net/sctp/sm_make_chunk.c in the SCTP network protocol in the Linux kernel with a local user privilege access. In this flaw, an attempt to use more buffer than is allocated triggers a BUG_ON issue, leading to a denial of service (DOS). | ||||
| CVE-2021-41272 | 1 Linuxfoundation | 1 Besu | 2024-11-21 | 7.5 High |
| Besu is an Ethereum client written in Java. Starting in version 21.10.0, changes in the implementation of the SHL, SHR, and SAR operations resulted in the introduction of a signed type coercion error in values that represent negative values for 32 bit signed integers. Smart contracts that ask for shifts between approximately 2 billion and 4 billion bits (nonsensical but valid values for the operation) will fail to execute and hence fail to validate. In networks where vulnerable versions are mining with other clients or non-vulnerable versions this will result in a fork and the relevant transactions will not be included in the fork. In networks where vulnerable versions are not mining (such as Rinkeby) no fork will result and the validator nodes will stop accepting blocks. In networks where only vulnerable versions are mining the relevant transaction will not be included in any blocks. When the network adds a non-vulnerable version the network will act as in the first case. Besu 21.10.2 contains a patch for this issue. Besu 21.7.4 is not vulnerable and clients can roll back to that version. There is a workaround available: Once a transaction with the relevant shift operations is included in the canonical chain, the only remediation is to make sure all nodes are on non-vulnerable versions. | ||||
| CVE-2021-41202 | 1 Google | 1 Tensorflow | 2024-11-21 | 5.5 Medium |
| TensorFlow is an open source platform for machine learning. In affected versions while calculating the size of the output within the `tf.range` kernel, there is a conditional statement of type `int64 = condition ? int64 : double`. Due to C++ implicit conversion rules, both branches of the condition will be cast to `double` and the result would be truncated before the assignment. This result in overflows. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | ||||
| CVE-2021-3444 | 3 Canonical, Debian, Linux | 3 Ubuntu Linux, Debian Linux, Linux Kernel | 2024-11-21 | 7.8 High |
| The bpf verifier in the Linux kernel did not properly handle mod32 destination register truncation when the source register was known to be 0. A local attacker with the ability to load bpf programs could use this gain out-of-bounds reads in kernel memory leading to information disclosure (kernel memory), and possibly out-of-bounds writes that could potentially lead to code execution. This issue was addressed in the upstream kernel in commit 9b00f1b78809 ("bpf: Fix truncation handling for mod32 dst reg wrt zero") and in Linux stable kernels 5.11.2, 5.10.19, and 5.4.101. | ||||
| CVE-2021-38187 | 1 Anymap Project | 1 Anymap | 2024-11-21 | 9.8 Critical |
| An issue was discovered in the anymap crate through 0.12.1 for Rust. It violates soundness via conversion of a *u8 to a *u64. | ||||
| CVE-2021-37679 | 1 Google | 1 Tensorflow | 2024-11-21 | 7.1 High |
| TensorFlow is an end-to-end open source platform for machine learning. In affected versions it is possible to nest a `tf.map_fn` within another `tf.map_fn` call. However, if the input tensor is a `RaggedTensor` and there is no function signature provided, code assumes the output is a fully specified tensor and fills output buffer with uninitialized contents from the heap. The `t` and `z` outputs should be identical, however this is not the case. The last row of `t` contains data from the heap which can be used to leak other memory information. The bug lies in the conversion from a `Variant` tensor to a `RaggedTensor`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/ragged_tensor_from_variant_op.cc#L177-L190) does not check that all inner shapes match and this results in the additional dimensions. The same implementation can result in data loss, if input tensor is tweaked. We have patched the issue in GitHub commit 4e2565483d0ffcadc719bd44893fb7f609bb5f12. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | ||||
| CVE-2021-37669 | 1 Google | 1 Tensorflow | 2024-11-21 | 5.5 Medium |
| TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause denial of service in applications serving models using `tf.raw_ops.NonMaxSuppressionV5` by triggering a division by 0. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/image/non_max_suppression_op.cc#L170-L271) uses a user controlled argument to resize a `std::vector`. However, as `std::vector::resize` takes the size argument as a `size_t` and `output_size` is an `int`, there is an implicit conversion to unsigned. If the attacker supplies a negative value, this conversion results in a crash. A similar issue occurs in `CombinedNonMaxSuppression`. We have patched the issue in GitHub commit 3a7362750d5c372420aa8f0caf7bf5b5c3d0f52d and commit [b5cdbf12ffcaaffecf98f22a6be5a64bb96e4f58. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | ||||
| CVE-2021-37661 | 1 Google | 1 Tensorflow | 2024-11-21 | 5.5 Medium |
| TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause a denial of service in `boosted_trees_create_quantile_stream_resource` by using negative arguments. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/quantile_ops.cc#L96) does not validate that `num_streams` only contains non-negative numbers. In turn, [this results in using this value to allocate memory](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/quantiles/quantile_stream_resource.h#L31-L40). However, `reserve` receives an unsigned integer so there is an implicit conversion from a negative value to a large positive unsigned. This results in a crash from the standard library. We have patched the issue in GitHub commit 8a84f7a2b5a2b27ecf88d25bad9ac777cd2f7992. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | ||||
| CVE-2021-37646 | 1 Google | 1 Tensorflow | 2024-11-21 | 5.5 Medium |
| TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of `tf.raw_ops.StringNGrams` is vulnerable to an integer overflow issue caused by converting a signed integer value to an unsigned one and then allocating memory based on this value. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/string_ngrams_op.cc#L184) calls `reserve` on a `tstring` with a value that sometimes can be negative if user supplies negative `ngram_widths`. The `reserve` method calls `TF_TString_Reserve` which has an `unsigned long` argument for the size of the buffer. Hence, the implicit conversion transforms the negative value to a large integer. We have patched the issue in GitHub commit c283e542a3f422420cfdb332414543b62fc4e4a5. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | ||||
| CVE-2021-37645 | 1 Google | 1 Tensorflow | 2024-11-21 | 5.5 Medium |
| TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of `tf.raw_ops.QuantizeAndDequantizeV4Grad` is vulnerable to an integer overflow issue caused by converting a signed integer value to an unsigned one and then allocating memory based on this value. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L126) uses the `axis` value as the size argument to `absl::InlinedVector` constructor. But, the constructor uses an unsigned type for the argument, so the implicit conversion transforms the negative value to a large integer. We have patched the issue in GitHub commit 96f364a1ca3009f98980021c4b32be5fdcca33a1. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, and TensorFlow 2.4.3, as these are also affected and still in supported range. | ||||