CVE-2022-31264

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.

Source: CVE-2022-31264

CVE-2022-31259

The route lookup process in beego through 1.12.4 and 2.x through 2.0.2 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).

Source: CVE-2022-31259

CVE-2022-1752

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

Source: CVE-2022-1752

CVE-2022-29222

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.

Source: CVE-2022-29222

CVE-2022-29188

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.

Source: CVE-2022-29188

CVE-2022-29189

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.

Source: CVE-2022-29189

CVE-2022-29190

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.

Source: CVE-2022-29190

CVE-2022-29209

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.

Source: CVE-2022-29209

CVE-2022-29210

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.

Source: CVE-2022-29210

CVE-2022-29211

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.

Source: CVE-2022-29211