CVE Database

TensorFlow is an end-to-end open source platform for machine learning. Specifying a negative dense shape in `tf.raw_ops.SparseCountSparseOutput` results in a segmentation fault being thrown out from the standard library as `std::vector` invariants are broken. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L199-L213) assumes the first element of the dense shape is always positive and uses it to initialize a `BatchedMap<T>` (i.e., `std::vector<absl::flat_hash_map<int64,T>>`(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L27)) data structure. If the `shape` tensor has more than one element, `num_batches` is the first value in `shape`. Ensuring that the `dense_shape` argument is a valid tensor shape (that is, all elements are non-negative) solves this issue. 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.

TensorFlow is an end-to-end open source platform for machine learning. Missing validation between arguments to `tf.raw_ops.Conv3DBackprop*` operations can result in heap buffer overflows. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/4814fafb0ca6b5ab58a09411523b2193fed23fed/tensorflow/core/kernels/conv_grad_shape_utils.cc#L94-L153) assumes that the `input`, `filter_sizes` and `out_backprop` tensors have the same shape, as they are accessed in parallel. 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. The API of `tf.raw_ops.SparseCross` allows combinations which would result in a `CHECK`-failure and denial of service. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/3d782b7d47b1bf2ed32bd4a246d6d6cadc4c903d/tensorflow/core/kernels/sparse_cross_op.cc#L114-L116) is tricked to consider a tensor of type `tstring` which in fact contains integral elements. Fixing the type confusion by preventing mixing `DT_STRING` and `DT_INT64` types solves this issue. 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. In eager mode (default in TF 2.0 and later), session operations are invalid. However, users could still call the raw ops associated with them and trigger a null pointer dereference. The implementation(https://github.com/tensorflow/tensorflow/blob/eebb96c2830d48597d055d247c0e9aebaea94cd5/tensorflow/core/kernels/session_ops.cc#L104) dereferences the session state pointer without checking if it is valid. Thus, in eager mode, `ctx->session_state()` is nullptr and the call of the member function is undefined behavior. 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. A malicious user could trigger a division by 0 in `Conv3D` implementation. The implementation(https://github.com/tensorflow/tensorflow/blob/42033603003965bffac51ae171b51801565e002d/tensorflow/core/kernels/conv_ops_3d.cc#L143-L145) does a modulo operation based on user controlled input. Thus, when `filter` has a 0 as the fifth element, this results in a division by 0. Additionally, if the shape of the two tensors is not valid, an Eigen assertion can be triggered, resulting in a program crash. 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. Calling `tf.raw_ops.RaggedTensorToVariant` with arguments specifying an invalid ragged tensor results in a null pointer dereference. The implementation of `RaggedTensorToVariant` operations(https://github.com/tensorflow/tensorflow/blob/904b3926ed1c6c70380d5313d282d248a776baa1/tensorflow/core/kernels/ragged_tensor_to_variant_op.cc#L39-L40) does not validate that the ragged tensor argument is non-empty. Since `batched_ragged` contains no elements, `batched_ragged.splits` is a null vector, thus `batched_ragged.splits(0)` will result in dereferencing `nullptr`. 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. The implementation of `MatrixDiag*` operations(https://github.com/tensorflow/tensorflow/blob/4c4f420e68f1cfaf8f4b6e8e3eb857e9e4c3ff33/tensorflow/core/kernels/linalg/matrix_diag_op.cc#L195-L197) does not validate that the tensor arguments are non-empty. 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. If the `splits` argument of `RaggedBincount` does not specify a valid `SparseTensor`(https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor), then an attacker can trigger a heap buffer overflow. This will cause a read from outside the bounds of the `splits` tensor buffer in the implementation of the `RaggedBincount` op(https://github.com/tensorflow/tensorflow/blob/8b677d79167799f71c42fd3fa074476e0295413a/tensorflow/core/kernels/bincount_op.cc#L430-L446). Before the `for` loop, `batch_idx` is set to 0. The attacker sets `splits(0)` to be 7, hence the `while` loop does not execute and `batch_idx` remains 0. This then results in writing to `out(-1, bin)`, which is before the heap allocated buffer for the output 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 also affected.

TensorFlow is an end-to-end open source platform for machine learning. Calling TF operations with tensors of non-numeric types when the operations expect numeric tensors result in null pointer dereferences. The conversion from Python array to C++ array(https://github.com/tensorflow/tensorflow/blob/ff70c47a396ef1e3cb73c90513da4f5cb71bebba/tensorflow/python/lib/core/ndarray_tensor.cc#L113-L169) is vulnerable to a type confusion. 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.

In ImageMagick versions before 7.0.9-0, there are outside the range of representable values of type 'float' at MagickCore/quantize.c.

TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a denial of service via a FPE runtime error in `tf.raw_ops.DenseCountSparseOutput`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/efff014f3b2d8ef6141da30c806faf141297eca1/tensorflow/core/kernels/count_ops.cc#L123-L127) computes a divisor value from user data but does not check that the result is 0 before doing the division. Since `data` is given by the `values` argument, `num_batch_elements` is 0. 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 also affected.

TensorFlow is an end-to-end open source platform for machine learning. If the `splits` argument of `RaggedBincount` does not specify a valid `SparseTensor`(https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor), then an attacker can trigger a heap buffer overflow. This will cause a read from outside the bounds of the `splits` tensor buffer in the implementation of the `RaggedBincount` op(https://github.com/tensorflow/tensorflow/blob/8b677d79167799f71c42fd3fa074476e0295413a/tensorflow/core/kernels/bincount_op.cc#L430-L433). Before the `for` loop, `batch_idx` is set to 0. The user controls the `splits` array, making it contain only one element, 0. Thus, the code in the `while` loop would increment `batch_idx` and then try to read `splits(1)`, which is outside of bounds. 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 also affected.

IBM QRadar User Behavior Analytics 1.0.0 through 4.1.0 allows web pages to be stored locally which can be read by another user on the system. IBM X-Force ID: 195999.

IBM Cloud Pak for Security (CP4S) 1.4.0.0, 1.5.0.0, 1.5.0.1, 1.6.0.0, and 1.6.0.1 could allow a privileged user to inject inject malicious data using a specially crafted HTTP request due to improper input validation.

Pydantic is a data validation and settings management using Python type hinting. In affected versions passing either `'infinity'`, `'inf'` or `float('inf')` (or their negatives) to `datetime` or `date` fields causes validation to run forever with 100% CPU usage (on one CPU). Pydantic has been patched with fixes available in the following versions: v1.8.2, v1.7.4, v1.6.2. All these versions are available on pypi(https://pypi.org/project/pydantic/#history), and will be available on conda-forge(https://anaconda.org/conda-forge/pydantic) soon. See the changelog(https://pydantic-docs.helpmanual.io/) for details. If you absolutely can't upgrade, you can work around this risk using a validator(https://pydantic-docs.helpmanual.io/usage/validators/) to catch these values. This is not an ideal solution (in particular you'll need a slightly different function for datetimes), instead of a hack like this you should upgrade pydantic. If you are not using v1.8.x, v1.7.x or v1.6.x and are unable to upgrade to a fixed version of pydantic, please create an issue at https://github.com/samuelcolvin/pydantic/issues requesting a back-port, and we will endeavour to release a patch for earlier versions of pydantic.

An issue was discovered in the Headunit NTG6 in the MBUX Infotainment System on Mercedes-Benz vehicles through 2021. A type confusion issue affects MultiSvSetAttributes in the HiQnet Protocol, leading to remote code execution.

An issue was discovered in the Headunit NTG6 in the MBUX Infotainment System on Mercedes-Benz vehicles through 2021. The count in MultiSvGet, GetAttributes, and MultiSvSet is not checked in the HiQnet Protocol, leading to remote code execution.

An issue was discovered in the Headunit NTG6 in the MBUX Infotainment System on Mercedes-Benz vehicles through 2021. A Message Length is not checked in the HiQnet Protocol, leading to remote code execution.

In Logstash versions after 6.4.0 and before 6.8.15 and 7.12.0 a TLS certificate validation flaw was found in the monitoring feature. When specifying a trusted server CA certificate Logstash would not properly verify the certificate returned by the monitoring server. This could result in a man in the middle style attack against the Logstash monitoring data.

In Kibana versions before 7.12.0 and 6.8.15 a flaw in the session timeout was discovered where the xpack.security.session.idleTimeout setting is not being respected. This was caused by background polling activities unintentionally extending authenticated users sessions, preventing a user session from timing out.

Exiv2 is a C++ library and a command-line utility to read, write, delete and modify Exif, IPTC, XMP and ICC image metadata. A read of uninitialized memory was found in Exiv2 versions v0.27.3 and earlier. Exiv2 is a command-line utility and C++ library for reading, writing, deleting, and modifying the metadata of image files. The read of uninitialized memory is triggered when Exiv2 is used to read the metadata of a crafted image file. An attacker could potentially exploit the vulnerability to leak a few bytes of stack memory, if they can trick the victim into running Exiv2 on a crafted image file. The bug is fixed in version v0.27.4.

A possible use-after-free and double-free in c-ares lib version 1.16.0 if ares_destroy() is called prior to ares_getaddrinfo() completing. This flaw possibly allows an attacker to crash the service that uses c-ares lib. The highest threat from this vulnerability is to this service availability.

The 802.11 standard that underpins Wi-Fi Protected Access (WPA, WPA2, and WPA3) and Wired Equivalent Privacy (WEP) doesn't require that the A-MSDU flag in the plaintext QoS header field is authenticated. Against devices that support receiving non-SSP A-MSDU frames (which is mandatory as part of 802.11n), an adversary can abuse this to inject arbitrary network packets.

The 802.11 standard that underpins Wi-Fi Protected Access (WPA, WPA2, and WPA3) and Wired Equivalent Privacy (WEP) doesn't require that all fragments of a frame are encrypted under the same key. An adversary can abuse this to decrypt selected fragments when another device sends fragmented frames and the WEP, CCMP, or GCMP encryption key is periodically renewed.

The 802.11 standard that underpins Wi-Fi Protected Access (WPA, WPA2, and WPA3) and Wired Equivalent Privacy (WEP) doesn't require that received fragments be cleared from memory after (re)connecting to a network. Under the right circumstances, when another device sends fragmented frames encrypted using WEP, CCMP, or GCMP, this can be abused to inject arbitrary network packets and/or exfiltrate user data.

Synapse is a Matrix reference homeserver written in python (pypi package matrix-synapse). Matrix is an ecosystem for open federated Instant Messaging and VoIP. In Synapse before version 1.33.2 "Push rules" can specify conditions under which they will match, including `event_match`, which matches event content against a pattern including wildcards. Certain patterns can cause very poor performance in the matching engine, leading to a denial-of-service when processing moderate length events. The issue is patched in version 1.33.2. A potential workaround might be to prevent users from making custom push rules, by blocking such requests at a reverse-proxy.

In JetBrains TeamCity before 2020.2.2, audit logs were not sufficient when an administrator uploaded a file.

Information disclosure in the TeamCity plugin for IntelliJ before 2020.2.2.85899 was possible because a local temporary file had Insecure Permissions.

This vulnerability allows remote attackers to disclose sensitive information on affected installations of Foxit Reader 10.1.1.37576. User interaction is required to exploit this vulnerability in that the target must visit a malicious page or open a malicious file. The specific flaw exists within the handling of U3D objects embedded in PDF files. The issue results from the lack of proper validation of user-supplied data, which can result in a read past the end of an allocated object. An attacker can leverage this in conjunction with other vulnerabilities to execute arbitrary code in the context of the current process. Was ZDI-CAN-12936.

This vulnerability allows remote attackers to disclose sensitive information on affected installations of Foxit Reader 10.1.3.37598. User interaction is required to exploit this vulnerability in that the target must visit a malicious page or open a malicious file. The specific flaw exists within the handling of U3D files embedded in PDF documents. The issue results from the lack of proper validation of user-supplied data, which can result in a read past the end of an allocated object. An attacker can leverage this in conjunction with other vulnerabilities to execute arbitrary code in the context of the current process. Was ZDI-CAN-13621.