CVE’s

TensorFlow is an open source platform for machine learning. When converting transposed convolutions using per-channel weight quantization the converter segfaults and crashes the Python process. We have patched the issue in GitHub commit aa0b852a4588cea4d36b74feb05d93055540b450. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

TensorFlow is an open source platform for machine learning. If `QuantizeAndDequantizeV3` is given a nonscalar `num_bits` input tensor, it results in a `CHECK` fail that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit f3f9cb38ecfe5a8a703f2c4a8fead434ef291713. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

Besu is a Java-based Ethereum client. In versions newer than 22.1.3 and prior to 22.7.1, Besu is subject to an Incorrect Conversion between Numeric Types. An error in 32 bit signed and unsigned types in the calculation of available gas in the CALL operations (including DELEGATECALL) results in incorrect gas being passed into called contracts and incorrect gas being returned after call execution. Where the amount of gas makes a difference in the success or failure, or if the gas is a negative 64 bit value, the execution will result in a different state root than expected, resulting in a consensus failure in networks with multiple EVM implementations. In networks with a single EVM implementation this can be used to execute with significantly more gas than then transaction requested, possibly exceeding gas limitations. This issue is patched in version 22.7.1. As a workaround, reverting to version 22.1.3 or earlier will prevent incorrect execution.

py-cord is a an API wrapper for Discord written in Python. Bots creating using py-cord version 2.0.0 are vulnerable to remote shutdown if they are added to the server with the `application.commands` scope without the `bot` scope. Currently, it appears that all public bots that use slash commands are affected. This issue has been patched in version 2.0.1. There are currently no recommended workarounds - please upgrade to a patched version.

Hyperledger Fabric is an enterprise-grade permissioned distributed ledger framework for developing solutions and applications. If a gateway client application sends a malformed request to a gateway peer it may crash the peer node. Version 2.4.6 checks for the malformed gateway request and returns an error to the gateway client. There are no known workarounds, users must upgrade to version 2.4.6.

Deeplearning4J is a suite of tools for deploying and training deep learning models using the JVM. Packages org.deeplearning4j:dl4j-examples and org.deeplearning4j:platform-tests through version 1.0.0-M2.1 may use some unclaimed S3 buckets in tests in examples. This is likely affect people who use some older NLP examples that reference an old S3 bucket. The problem has been patched. Users should upgrade to snapshots as Deeplearning4J plan to publish a release with the fix at a later date. As a workaround, download a word2vec google news vector from a new source using git lfs from here.

The typo3/html-sanitizer package is an HTML sanitizer, written in PHP, aiming to provide XSS-safe markup based on explicitly allowed tags, attributes and values. Due to a parsing issue in the upstream package `masterminds/html5`, malicious markup used in a sequence with special HTML comments cannot be filtered and sanitized. This allows for a bypass of the cross-site scripting mechanism of `typo3/html-sanitizer`. This issue has been addressed in versions 1.0.7 and 2.0.16 of the `typo3/html-sanitizer` package. Users are advised to upgrade. There are no known workarounds for this issue.

A buffer overrun can be triggered in X.509 certificate verification, specifically in name constraint checking. Note that this occurs after certificate chain signature verification and requires either a CA to have signed the malicious certificate or for the application to continue certificate verification despite failure to construct a path to a trusted issuer. An attacker can craft a malicious email address to overflow four attacker-controlled bytes on the stack. This buffer overflow could result in a crash (causing a denial of service) or potentially remote code execution. Many platforms implement stack overflow protections which would mitigate against the risk of remote code execution. The risk may be further mitigated based on stack layout for any given platform/compiler. Pre-announcements of CVE-2022-3602 described this issue as CRITICAL. Further analysis based on some of the mitigating factors described above have led this to be downgraded to HIGH. Users are still encouraged to upgrade to a new version as soon as possible. In a TLS client, this can be triggered by connecting to a malicious server. In a TLS server, this can be triggered if the server requests client authentication and a malicious client connects. Fixed in OpenSSL 3.0.7 (Affected 3.0.0,3.0.1,3.0.2,3.0.3,3.0.4,3.0.5,3.0.6).

TensorFlow is an open source platform for machine learning. If `FakeQuantWithMinMaxVarsPerChannel` is given `min` or `max` tensors of a rank other than one, it results in a `CHECK` fail that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 785d67a78a1d533759fcd2f5e8d6ef778de849e0. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

TensorFlow is an open source platform for machine learning. If `RaggedTensorToVariant` is given a `rt_nested_splits` list that contains tensors of ranks other than one, it results in a `CHECK` fail that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 88f93dfe691563baa4ae1e80ccde2d5c7a143821. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.