CVE Feed

The Turn off all comments WordPress plugin through 1.0 does not sanitise and escape the rows parameter before outputting it back in an admin page, leading to a Reflected Cross-Site Scripting

The WP Meta SEO WordPress plugin before 4.4.7 does not sanitise or escape the breadcrumb separator before outputting it to the page, allowing a high privilege user such as an administrator to inject arbitrary javascript into the page even when unfiltered html is disallowed.

The WP Contacts Manager WordPress plugin through 2.2.4 fails to properly sanitize user supplied POST data before it is being interpolated in an SQL statement and then executed, leading to an SQL injection vulnerability.

The Nirweb support WordPress plugin before 2.8.2 does not sanitise and escape a parameter before using it in a SQL statement via an AJAX action (available to unauthenticated users), leading to an SQL injection

The XML Sitemap Generator for Google WordPress plugin before 2.0.4 does not validate a parameter which can be set to an arbitrary value, thus causing XSS via error message or RCE if allow_url_include is turned on.

JFrog Artifactory prior to version 7.28.0 and 6.23.38, is vulnerable to Broken Access Control, the copy functionality can be used by a low-privileged user to read and copy any artifact that exists in the Artifactory deployment due to improper permissions validation.

Authorization Bypass Through User-Controlled Key in GitHub repository publify/publify prior to 9.2.9.

Out-of-bounds Read in GitHub repository vim/vim prior to 8.2.

OS Command Injection in GitHub repository yogeshojha/rengine prior to 1.2.0.

A flaw was found in libtiff 4.4.0rc1. There is a sysmalloc assertion fail in rotateImage() at tiffcrop.c:8621 that can cause program crash when reading a crafted input.

It was found in libtiff 4.4.0rc1 that there is an invalid pointer free operation in TIFFClose() at tif_close.c:131 called by tiffcrop.c:2522 that can cause a program crash and denial of service while processing crafted input.

LibTIFF 4.4.0 has an out-of-bounds write in _TIFFmemset in libtiff/tif_unix.c:340 when called from processCropSelections, tools/tiffcrop.c:7619, allowing attackers to cause a denial-of-service via a crafted tiff file. For users that compile libtiff from sources, the fix is available with commit 236b7191.

Buffer Over-read in GitHub repository vim/vim prior to 8.2.

There is a double free or corruption in rotateImage() at tiffcrop.c:8839 found in libtiff 4.4.0rc1

Access of Uninitialized Pointer in GitHub repository radareorg/radare2 prior to 5.7.0.

Gitblit 1.9.2 allows privilege escalation via the Config User Service: a control character can be placed in a profile data field, such as an emailAddress%3Atext '[email protected]\n\trole = "#admin"' value.

A Path Traversal vulnerability in Gitblit 1.9.3 can lead to reading website files via /resources//../ (e.g., followed by a WEB-INF or META-INF pathname).

Solana solana_rbpf before 0.2.29 has an addition integer overflow via invalid ELF program headers. elf.rs has a panic via a malformed eBPF program.

Unrestricted Upload of File with Dangerous Type in GitHub repository polonel/trudesk prior to 1.2.2.

RegionProtect is a plugin that allows users to manage certain events in certain regions of the world. Versions prior to 1.1.0 contain a YAML injection vulnerability that can cause an instant server crash if the passed arguments are not matched. Version 1.1.0 contains a patch for this issue. As a workaround, restrict operator permissions to untrusted people and avoid entering arguments likely to cause a crash.

Pion DTLS is a Go implementation of Datagram Transport Layer Security. Prior to version 2.1.5, a DTLS Client could provide a Certificate that it doesn't posses the private key for and Pion DTLS wouldn't reject it. This issue affects users that are using Client certificates only. The connection itself is still secure. The Certificate provided by clients can't be trusted when using a Pion DTLS server prior to version 2.1.5. Users should upgrade to version 2.1.5 to receive a patch. There are currently no known workarounds.

The route lookup process in beego before 1.12.9 and 2.x before 2.0.3 allows attackers to bypass access control. When a /p1/p2/:name route is configured, attackers can access it by appending .xml in various places (e.g., p1.xml instead of p1).

Pion DTLS is a Go implementation of Datagram Transport Layer Security. Prior to version 2.1.4, a buffer that was used for inbound network traffic had no upper limit. Pion DTLS would buffer all network traffic from the remote user until the handshake completes or timed out. An attacker could exploit this to cause excessive memory usage. Version 2.1.4 contains a patch for this issue. There are currently no known workarounds available.

Pion DTLS is a Go implementation of Datagram Transport Layer Security. Prior to version 2.1.4, an attacker can send packets that sends Pion DTLS into an infinite loop when processing. Version 2.1.4 contains a patch for this issue. There are currently no known workarounds available.

Smokescreen is an HTTP proxy. The primary use case for Smokescreen is to prevent server-side request forgery (SSRF) attacks in which external attackers leverage the behavior of applications to connect to or scan internal infrastructure. Smokescreen also offers an option to deny access to additional (e.g., external) URLs by way of a deny list. There was an issue in Smokescreen that made it possible to bypass the deny list feature by surrounding the hostname with square brackets (e.g. `[example.com]`). This only impacted the HTTP proxy functionality of Smokescreen. HTTPS requests were not impacted. Smokescreen version 0.0.4 contains a patch for this issue.

NextAuth.js (next-auth) is am open source authentication solution for Next.js applications. Prior to versions 3.29.3 and 4.3.3, an open redirect vulnerability is present when the developer is implementing an OAuth 1 provider. Versions 3.29.3 and 4.3.3 contain a patch for this issue. The maintainers recommend adding a certain configuration to one's `callbacks` option as a workaround for those unable to upgrade.

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, TensorFlow's `saved_model_cli` tool is vulnerable to a code injection. This can be used to open a reverse shell. This code path was maintained for compatibility reasons as the maintainers had several test cases where numpy expressions were used as arguments. However, given that the tool is always run manually, the impact of this is still not severe. The maintainers have now removed the `safe=False` argument, so all parsing is done without calling `eval`. The patch is available in versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4.

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the `tf.compat.v1.signal.rfft2d` and `tf.compat.v1.signal.rfft3d` lack input validation and under certain condition can result in crashes (due to `CHECK`-failures). Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

TensorFlow is an open source platform for machine learning. In version 2.8.0, the `TensorKey` hash function used total estimated `AllocatedBytes()`, which (a) is an estimate per tensor, and (b) is a very poor hash function for constants (e.g. `int32_t`). It also tried to access individual tensor bytes through `tensor.data()` of size `AllocatedBytes()`. This led to ASAN failures because the `AllocatedBytes()` is an estimate of total bytes allocated by a tensor, including any pointed-to constructs (e.g. strings), and does not refer to contiguous bytes in the `.data()` buffer. The discoverers could not use this byte vector anyway because types such as `tstring` include pointers, whereas they needed to hash the string values themselves. This issue is patched in Tensorflow versions 2.9.0 and 2.8.1.

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the macros that TensorFlow uses for writing assertions (e.g., `CHECK_LT`, `CHECK_GT`, etc.) have an incorrect logic when comparing `size_t` and `int` values. Due to type conversion rules, several of the macros would trigger incorrectly. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.histogram_fixed_width` is vulnerable to a crash when the values array contain `Not a Number` (`NaN`) elements. The implementation assumes that all floating point operations are defined and then converts a floating point result to an integer index. If `values` contains `NaN` then the result of the division is still `NaN` and the cast to `int32` would result in a crash. This only occurs on the CPU implementation. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, certain TFLite models that were created using TFLite model converter would crash when loaded in the TFLite interpreter. The culprit is that during quantization the scale of values could be greater than 1 but code was always assuming sub-unit scaling. Thus, since code was calling `QuantizeMultiplierSmallerThanOneExp`, the `TFLITE_CHECK_LT` assertion would trigger and abort the process. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.QuantizedConv2D` does not fully validate the input arguments. In this case, references get bound to `nullptr` for each argument that is empty. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.ragged.constant` does not fully validate the input arguments. This results in a denial of service by consuming all available memory. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.SpaceToBatchND` (in all backends such as XLA and handwritten kernels) is vulnerable to an integer overflow: The result of this integer overflow is used to allocate the output tensor, hence we get a denial of service via a `CHECK`-failure (assertion failure), as in TFSA-2021-198. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.UnsortedSegmentJoin` does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. The code assumes `num_segments` is a positive scalar but there is no validation. Since this value is used to allocate the output tensor, a negative value would result in a `CHECK`-failure (assertion failure), as per TFSA-2021-198. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

Weak Password Requirements in GitHub repository polonel/trudesk prior to 1.2.2.

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.EditDistance` has incomplete validation. Users can pass negative values to cause a segmentation fault based denial of service. In multiple places throughout the code, one may compute an index for a write operation. However, the existing validation only checks against the upper bound of the array. Hence, it is possible to write before the array by massaging the input to generate negative values for `loc`. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, there is a potential for segfault / denial of service in TensorFlow by calling `tf.compat.v1.*` ops which don't yet have support for quantized types, which was added after migration to TensorFlow 2.x. In these scenarios, since the kernel is missing, a `nullptr` value is passed to `ParseDimensionValue` for the `py_value` argument. Then, this is dereferenced, resulting in segfault. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.SparseTensorDenseAdd` does not fully validate the input arguments. In this case, a reference gets bound to a `nullptr` during kernel execution. This is undefined behavior. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, multiple TensorFlow operations misbehave in eager mode when the resource handle provided to them is invalid. In graph mode, it would have been impossible to perform these API calls, but migration to TF 2.x eager mode opened up this vulnerability. If the resource handle is empty, then a reference is bound to a null pointer inside TensorFlow codebase (various codepaths). This is undefined behavior. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

In Checkmk before 1.6.0p29, 2.x before 2.0.0p25, and 2.1.x before 2.1.0b10, a site user can escalate to root by editing an OMD hook symlink.

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.StagePeek` does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. The code assumes `index` is a scalar but there is no validation for this before accessing its value. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.UnsortedSegmentJoin` does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. The code assumes `num_segments` is a scalar but there is no validation for this before accessing its value. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.Conv3DBackpropFilterV2` does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. The code does not validate that the `filter_sizes` argument is a vector. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.SparseTensorToCSRSparseMatrix` does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. The code assumes `dense_shape` is a vector and `indices` is a matrix (as part of requirements for sparse tensors) but there is no validation for this. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

Improper Restriction of Rendered UI Layers or Frames in GitHub repository polonel/trudesk prior to 1.2.2.

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.LoadAndRemapMatrix does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. The code assumes `initializing_values` is a vector but there is no validation for this before accessing its value. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.LSTMBlockCell` does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. The code does not validate the ranks of any of the arguments to this API call. This results in `CHECK`-failures when the elements of the tensor are accessed. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.TensorSummaryV2` does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.