Improper Initialization

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.Show less

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.Show less

A memory initialization issue was addressed with improved memory handling. This issue is fixed in Security Update 2021-002 Catalina, Security Update 2021-003 Mojave, iOS 14.5 and iPadOS 14.5, watchOS 7.4, tvOS 14.5, macOS Big Sur 11.3. A malicious application may be able to disclose kernel memory.Show less

A memory initialization issue was addressed with improved memory handling. This issue is fixed in iTunes 12.11.3 for Windows, Security Update 2021-002 Catalina, Security Update 2021-003 Mojave, iCloud for Windows 12.3, macOS Big Sur 11.3, watchOS 7.4, tvOS 14.5, iOS 14.5 and iPadOS 14.5. Processing maliciously crafted web content may disclose sensitive user information.Show less

A memory initialization issue was addressed with improved memory handling. This issue is fixed in macOS Big Sur 11.3, iOS 14.5 and iPadOS 14.5, watchOS 7.4, tvOS 14.5. Processing maliciously crafted web content may result in the disclosure of process memory.Show less

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.Show less

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.Show less

Due to an Improper Initialization vulnerability in Juniper Networks Junos OS on PTX platforms and QFX10K Series with Paradise (PE) chipset-based line cards, ddos-protection configuration changes made from the CLI will not take effect as expected beyond the default DDoS (Distributed Denial of Service) settings in the Packet Forwarding Engine (PFE). This may cause BFD sessions to flap when a high rate of specific packets are received. Flapping of BFD sessions in turn may impact routing protocols and network stability, leading to a Denial of Service (DoS) condition. Continued receipt and processing of this packet will create a sustained Denial of Service (DoS) condition. This issue affects only the following platforms with Paradise (PE) chipset-based line cards: PTX1000, PTX3000 (NextGen), PTX5000, PTX10008, PTX10016 Series and QFX10002 Series. This issue affects: Juniper Networks Junos OS 17.4 versions prior to 17.4R3-S5 on PTX Series, QFX10K Series; 18.2 versions prior to 18.2R3-S8 on PTX Series, QFX10K Series; 18.3 versions prior to 18.3R3-S5 on PTX Series, QFX10K Series; 18.4 versions prior to 18.4R2-S8 on PTX Series, QFX10K Series; 19.1 versions prior to 19.1R3-S5 on PTX Series, QFX10K Series; 19.2 versions prior to 19.2R3-S2 on PTX Series, QFX10K Series; 19.3 versions prior to 19.3R3-S2 on PTX Series, QFX10K Series; 19.4 versions prior to 19.4R3-S2 on PTX Series, QFX10K Series; 20.1 versions prior to 20.1R3 on PTX Series, QFX10K Series; 20.2 versions prior to 20.2R2-S3, 20.2R3 on PTX Series, QFX10K Series; 20.3 versions prior to 20.3R2 on PTX Series, QFX10K Series; 20.4 versions prior to 20.4R2 on PTX Series, QFX10K Series.Show less

Improper initialization in the BMC firmware for some Intel(R) Server Boards, Server Systems and Compute Modules before version 2.48.ce3e3bd2 may allow an authenticated user to potentially enable denial of service via local access.Show less

Improper initialization in a subsystem in the Intel(R) CSME versions before 11.8.86, 11.12.86, 11.22.86, 12.0.81, 13.0.47, 13.30.17, 14.1.53, 14.5.32, 13.50.11 and 15.0.22 may allow a privileged user to potentially enable information disclosure via local access.Show less

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.Show less

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.Show less

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.Show less

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.Show less

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.Show less

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.Show less