| CVE |
Vendors |
Products |
Updated |
CVSS v3.1 |
| This vulnerability allows local attackers to disclose sensitive information on affected installations of Parallels Desktop 15.1.5-47309. An attacker must first obtain the ability to execute low-privileged code on the target guest system in order to exploit this vulnerability. The specific flaw exists within the Open Tools Gate component. The issue results from the lack of proper locking when performing operations on an object. An attacker can leverage this in conjunction with other vulnerabilities to escalate privileges and execute arbitrary code in the context of the hypervisor. Was ZDI-CAN-13082. |
| This vulnerability allows local attackers to escalate privileges on affected installations of Parallels Desktop 16.1.1-49141. An attacker must first obtain the ability to execute high-privileged code on the target guest system in order to exploit this vulnerability. The specific flaw exists within the e1000e virtual device. The issue results from the lack of proper locking when performing operations on an object. An attacker can leverage this vulnerability to escalate privileges and execute arbitrary code in the context of the hypervisor. Was ZDI-CAN-12527. |
| Overly relaxed configuration of frontend resources server in Vaadin Designer versions 4.3.0 through 4.6.3 allows remote attackers to access project sources via crafted HTTP request. |
| Vulnerability in OSGi integration in com.vaadin:flow-server versions 1.2.0 through 2.4.7 (Vaadin 12.0.0 through 14.4.9), and 6.0.0 through 6.0.1 (Vaadin 19.0.0) allows attacker to access application classes and resources on the server via crafted HTTP request. |
| pleaseedit in please before 0.4 uses predictable temporary filenames in /tmp and the target directory. This allows a local attacker to gain full root privileges by staging a symlink attack. |
| A memory initialization issue was addressed with improved memory handling. This issue is fixed in tvOS 15.2, macOS Big Sur 11.6.2. Parsing a maliciously crafted audio file may lead to disclosure of user information. |
| A logic issue was addressed with improved state management. This issue is fixed in iOS 14.5 and iPadOS 14.5. A user's password may be visible onscreen. |
| A sync issue was addressed with improved state validation. This issue is fixed in macOS Monterey 12.0.1. A user's messages may continue to sync after the user has signed out of iMessage. |
| Kaseya VSA before 9.5.7 allows attackers to bypass the 2FA requirement. The need to use 2FA for authentication in enforce client-side instead of server-side and can be bypassed using a local proxy. Thus rendering 2FA useless. Detailed description --- During the login process, after the user authenticates with username and password, the server sends a response to the client with the booleans MFARequired and MFAEnroled. If the attacker has obtained a password of a user and used an intercepting proxy (e.g. Burp Suite) to change the value of MFARequered from True to False, there is no prompt for the second factor, but the user is still logged in. |
| Firefox used to cache the last filename used for printing a file. When generating a filename for printing, Firefox usually suggests the web page title. The caching and suggestion techniques combined may have lead to the title of a website visited during private browsing mode being stored on disk. This vulnerability affects Firefox < 89. |
| An issue was discovered in the Linux kernel before 5.11.11. The netfilter subsystem allows attackers to cause a denial of service (panic) because net/netfilter/x_tables.c and include/linux/netfilter/x_tables.h lack a full memory barrier upon the assignment of a new table value, aka CID-175e476b8cdf. |
| An issue was discovered in the Linux kernel before 5.11.11. qrtr_recvmsg in net/qrtr/qrtr.c allows attackers to obtain sensitive information from kernel memory because of a partially uninitialized data structure, aka CID-50535249f624. |
| TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.io.decode_raw` produces incorrect results and crashes the Python interpreter when combining `fixed_length` and wider datatypes. The implementation of the padded version(https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc) is buggy due to a confusion about pointer arithmetic rules. First, the code computes(https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc#L61) the width of each output element by dividing the `fixed_length` value to the size of the type argument. The `fixed_length` argument is also used to determine the size needed for the output tensor(https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc#L63-L79). This is followed by reencoding code(https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc#L85-L94). The erroneous code is the last line above: it is moving the `out_data` pointer by `fixed_length * sizeof(T)` bytes whereas it only copied at most `fixed_length` bytes from the input. This results in parts of the input not being decoded into the output. Furthermore, because the pointer advance is far wider than desired, this quickly leads to writing to outside the bounds of the backing data. This OOB write leads to interpreter crash in the reproducer mentioned here, but more severe attacks can be mounted too, given that this gadget allows writing to periodically placed locations in memory. 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. Incomplete validation in `tf.raw_ops.CTCLoss` allows an attacker to trigger an OOB read from heap. The fix will be included in TensorFlow 2.5.0. We will also cherrypick these commits 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. Incomplete validation in `SparseReshape` results in a denial of service based on a `CHECK`-failure. The implementation(https://github.com/tensorflow/tensorflow/blob/e87b51ce05c3eb172065a6ea5f48415854223285/tensorflow/core/kernels/sparse_reshape_op.cc#L40) has no validation that the input arguments specify a valid sparse tensor. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 and TensorFlow 2.3.3, as these are the only affected versions. |
| TensorFlow is an end-to-end open source platform for machine learning. The validation in `tf.raw_ops.QuantizeAndDequantizeV2` allows invalid values for `axis` argument:. The validation(https://github.com/tensorflow/tensorflow/blob/eccb7ec454e6617738554a255d77f08e60ee0808/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L74-L77) uses `||` to mix two different conditions. If `axis_ < -1` the condition in `OP_REQUIRES` will still be true, but this value of `axis_` results in heap underflow. This allows attackers to read/write to other data on the heap. 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. Incomplete validation in `SparseAdd` results in allowing attackers to exploit undefined behavior (dereferencing null pointers) as well as write outside of bounds of heap allocated data. The implementation(https://github.com/tensorflow/tensorflow/blob/656e7673b14acd7835dc778867f84916c6d1cac2/tensorflow/core/kernels/sparse_add_op.cc) has a large set of validation for the two sparse tensor inputs (6 tensors in total), but does not validate that the tensors are not empty or that the second dimension of `*_indices` matches the size of corresponding `*_shape`. This allows attackers to send tensor triples that represent invalid sparse tensors to abuse code assumptions that are not protected by validation. 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. |
| Puma is a concurrent HTTP 1.1 server for Ruby/Rack applications. The fix for CVE-2019-16770 was incomplete. The original fix only protected existing connections that had already been accepted from having their requests starved by greedy persistent-connections saturating all threads in the same process. However, new connections may still be starved by greedy persistent-connections saturating all threads in all processes in the cluster. A `puma` server which received more concurrent `keep-alive` connections than the server had threads in its threadpool would service only a subset of connections, denying service to the unserved connections. This problem has been fixed in `puma` 4.3.8 and 5.3.1. Setting `queue_requests false` also fixes the issue. This is not advised when using `puma` without a reverse proxy, such as `nginx` or `apache`, because you will open yourself to slow client attacks (e.g. slowloris). The fix is very small and a git patch is available for those using unsupported versions of Puma. |
| In TP-Link Wireless N Router WR840N an ARP poisoning attack can cause buffer overflow |
| A race condition was discovered in get_old_root in fs/btrfs/ctree.c in the Linux kernel through 5.11.8. It allows attackers to cause a denial of service (BUG) because of a lack of locking on an extent buffer before a cloning operation, aka CID-dbcc7d57bffc. |
