Vulnerabilities (CVE)

Filtered by CWE-665
Total 323 CVE
CVE Vendors Products Updated CVSS v2 CVSS v3
CVE-2021-40025 1 Huawei 1 Harmonyos 2024-11-21 5.0 MEDIUM 7.5 HIGH
The eID module has a vulnerability that causes the memory to be used without being initialized,Successful exploitation of this vulnerability may affect data confidentiality.
CVE-2021-3565 3 Fedoraproject, Redhat, Tpm2-tools Project 3 Fedora, Enterprise Linux, Tpm2-tools 2024-11-21 4.3 MEDIUM 5.9 MEDIUM
A flaw was found in tpm2-tools in versions before 5.1.1 and before 4.3.2. tpm2_import used a fixed AES key for the inner wrapper, potentially allowing a MITM attacker to unwrap the inner portion and reveal the key being imported. The highest threat from this vulnerability is to data confidentiality.
CVE-2021-3329 1 Zephyrproject 1 Zephyr 2024-11-21 N/A 9.6 CRITICAL
Lack of proper validation in HCI Host stack initialization can cause a crash of the bluetooth stack
CVE-2021-39636 1 Google 1 Android 2024-11-21 2.1 LOW 4.4 MEDIUM
In do_ipt_get_ctl and do_ipt_set_ctl of ip_tables.c, there is a possible way to leak kernel information due to uninitialized data. This could lead to local information disclosure with system execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android kernelAndroid ID: A-120612905References: Upstream kernel
CVE-2021-36319 1 Dell 1 Networking Os10 2024-11-21 2.1 LOW 3.3 LOW
Dell Networking OS10 versions 10.4.3.x, 10.5.0.x and 10.5.1.x contain an information exposure vulnerability. A low privileged authenticated malicious user can gain access to SNMP authentication failure messages.
CVE-2021-36006 3 Adobe, Apple, Microsoft 3 Photoshop, Macos, Windows 2024-11-21 4.3 MEDIUM 3.3 LOW
Adobe Photoshop versions 21.2.9 (and earlier) and 22.4.2 (and earlier) are affected by an Improper input validation vulnerability when parsing a specially crafted file. An unauthenticated attacker could leverage this vulnerability to disclose arbitrary memory information in the context of the current user. Exploitation of this issue requires user interaction in that a victim must open a malicious file.
CVE-2021-35995 2 Adobe, Microsoft 2 After Effects, Windows 2024-11-21 4.3 MEDIUM 3.3 LOW
Adobe After Effects version 18.2.1 (and earlier) is affected by an Improper input validation vulnerability when parsing a specially crafted file. An unauthenticated attacker could leverage this vulnerability to disclose arbitrary memory information in the context of the current user. Exploitation of this issue requires user interaction in that a victim must open a malicious file.
CVE-2021-34703 1 Cisco 203 1000 Integrated Services Router, 1100-4g\/6g Integrated Services Router, 1100-4p Integrated Services Router and 200 more 2024-11-21 6.8 MEDIUM 6.8 MEDIUM
A vulnerability in the Link Layer Discovery Protocol (LLDP) message parser of Cisco IOS Software and Cisco IOS XE Software could allow an attacker to trigger a reload of an affected device, resulting in a denial of service (DoS) condition. This vulnerability is due to improper initialization of a buffer. An attacker could exploit this vulnerability via any of the following methods: An authenticated, remote attacker could access the LLDP neighbor table via either the CLI or SNMP while the device is in a specific state. An unauthenticated, adjacent attacker could corrupt the LLDP neighbor table by injecting specific LLDP frames into the network and then waiting for an administrator of the device or a network management system (NMS) managing the device to retrieve the LLDP neighbor table of the device via either the CLI or SNMP. An authenticated, adjacent attacker with SNMP read-only credentials or low privileges on the device CLI could corrupt the LLDP neighbor table by injecting specific LLDP frames into the network and then accessing the LLDP neighbor table via either the CLI or SNMP. A successful exploit could allow the attacker to cause the affected device to crash, resulting in a reload of the device.
CVE-2021-34697 1 Cisco 1 Ios Xe 2024-11-21 5.0 MEDIUM 5.8 MEDIUM
A vulnerability in the Protection Against Distributed Denial of Service Attacks feature of Cisco IOS XE Software could allow an unauthenticated, remote attacker to conduct denial of service (DoS) attacks to or through the affected device. This vulnerability is due to incorrect programming of the half-opened connections limit, TCP SYN flood limit, or TCP SYN cookie features when the features are configured in vulnerable releases of Cisco IOS XE Software. An attacker could exploit this vulnerability by attempting to flood traffic to or through the affected device. A successful exploit could allow the attacker to initiate a DoS attack to or through an affected device.
CVE-2021-33638 1 Openeuler 1 Isula 2024-11-21 N/A 8.4 HIGH
When the isula cp command is used to copy files from a container to a host machine and the container is controlled by an attacker, the attacker can escape the container.
CVE-2021-33637 1 Openeuler 1 Isula 2024-11-21 N/A 8.4 HIGH
When the isula export command is used to export a container to an image and the container is controlled by an attacker, the attacker can escape the container.
CVE-2021-33636 1 Openeuler 1 Isula 2024-11-21 N/A 8.4 HIGH
When the isula load command is used to load malicious images, attackers can execute arbitrary code.
CVE-2021-33635 1 Openeuler 1 Isula 2024-11-21 N/A 9.8 CRITICAL
When malicious images are pulled by isula pull, attackers can execute arbitrary code.
CVE-2021-33634 1 Openeuler 1 Icr 2024-11-21 N/A 6.3 MEDIUM
iSulad uses the lcr+lxc runtime (default) to run malicious images, which can cause DOS.
CVE-2021-30962 1 Apple 2 Macos, Tvos 2024-11-21 4.3 MEDIUM 5.5 MEDIUM
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.
CVE-2021-29614 1 Google 1 Tensorflow 2024-11-21 4.6 MEDIUM 7.1 HIGH
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.
CVE-2021-29613 1 Google 1 Tensorflow 2024-11-21 3.6 LOW 6.3 MEDIUM
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.
CVE-2021-29611 1 Google 1 Tensorflow 2024-11-21 2.1 LOW 3.6 LOW
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.
CVE-2021-29610 1 Google 1 Tensorflow 2024-11-21 4.6 MEDIUM 3.6 LOW
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.
CVE-2021-29609 1 Google 1 Tensorflow 2024-11-21 4.6 MEDIUM 5.3 MEDIUM
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.