The Image Hover Effects Css3 WordPress plugin through 4.5 does not sanitise and escape some of its settings, which could allow high privilege users such as admin to perform Stored Cross-Site Scripting attacks even when the unfiltered_html capability is disallowed (for example in multisite setup).

gomatrixserverlib is a Go library for matrix protocol federation. Dendrite is a Matrix homeserver written in Go, an alternative to Synapse. The power level parsing within gomatrixserverlib was failing to parse the `”events_default”` key of the `m.room.power_levels` event, defaulting the event default power level to zero in all cases. Power levels are the matrix terminology for user access level. In rooms where the `”events_default”` power level had been changed, this could result in events either being incorrectly authorised or rejected by Dendrite servers. gomatrixserverlib contains a fix as of commit `723fd49` and Dendrite 0.9.3 has been updated accordingly. Matrix rooms where the `”events_default”` power level has not been changed from the default of zero are not vulnerable. Users are advised to upgrade. There are no known workarounds for this issue.

Frontier is Substrate’s Ethereum compatibility layer. A security issue was discovered affecting parsing of the RPC result of the exit reason in case of EVM reversion. In release build, this would cause the exit reason being incorrectly parsed and returned by RPC. In debug build, this would cause an overflow panic. No action is needed unless you have a bridge node that needs to distinguish different reversion exit reasons and you used RPC for this. There are currently no known workarounds.

Venice is a Clojure inspired sandboxed Lisp dialect with excellent Java interoperability. A partial path traversal issue exists within the functions `load-file` and `load-resource`. These functions can be limited to load files from a list of load paths. Assuming Venice has been configured with the load paths: `[ “/Users/foo/resources” ]` When passing **relative** paths to these two vulnerable functions everything is fine: `(load-resource “test.png”)` => loads the file “/Users/foo/resources/test.png” `(load-resource “../resources-alt/test.png”)` => rejected, outside the load path When passing **absolute** paths to these two vulnerable functions Venice may return files outside the configured load paths: `(load-resource “/Users/foo/resources/test.png”)` => loads the file “/Users/foo/resources/test.png” `(load-resource “/Users/foo/resources-alt/test.png”)` => loads the file “/Users/foo/resources-alt/test.png” !!! The latter call suffers from the _Partial Path Traversal_ vulnerability. This issue’s scope is limited to absolute paths whose name prefix matches a load path. E.g. for a load-path `”/Users/foo/resources”`, the actor can cause loading a resource also from `”/Users/foo/resources-alt”`, but not from `”/Users/foo/images”`. Versions of Venice before and including v1.10.17 are affected by this issue. Upgrade to Venice >= 1.10.18, if you are on a version < 1.10.18. There are currently no known workarounds.

Arvados is an open source platform for managing, processing, and sharing genomic and other large scientific and biomedical data. A remote code execution (RCE) vulnerability in the Arvados Workbench allows authenticated attackers to execute arbitrary code via specially crafted JSON payloads. This exists in all versions up to 2.4.1 and is fixed in 2.4.2. This vulnerability is specific to the Ruby on Rails Workbench application (“Workbench 1”). We do not believe any other Arvados components, including the TypesScript browser-based Workbench application (“Workbench 2”) or API Server, are vulnerable to this attack. For versions of Arvados earlier than 2.4.2: remove the Ruby-based “Workbench 1” app (“apt-get remove arvados-workbench”) from your installation as a workaround.

TensorFlow is an open source platform for machine learning. When `tf.quantization.fake_quant_with_min_max_vars_gradient` receives input `min` or `max` that is nonscalar, it gives a `CHECK` fail that can trigger a denial of service attack. We have patched the issue in GitHub commit f3cf67ac5705f4f04721d15e485e192bb319feed. 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.