NVD-CWE-noinfo

TensorFlow is an open source platform for machine learning. If `RaggedBincount` is given an empty input tensor `splits`, it results in a segfault that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 7a4591fd4f065f4fa903593bc39b2f79530a74b8. 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.

This library allows strings to be parsed as functions and stored as a specialized component, [`JsonFunctionValue`](https://github.com/oxyno-zeta/react-editable-json-tree/blob/09a0ca97835b0834ad054563e2fddc6f22bc5d8c/src/components/JsonFunctionValue.js). To do this, Javascript’s [`eval`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/eval) function is used to execute strings that begin with “function” as Javascript. This unfortunately could allow arbitrary code to be executed if it exists as a value within the JSON structure being displayed. Given that this component may often be used to display data from arbitrary, untrusted sources, this is extremely dangerous. One important note is that users who have defined a custom [`onSubmitValueParser`](https://github.com/oxyno-zeta/react-editable-json-tree/tree/09a0ca97835b0834ad054563e2fddc6f22bc5d8c#onsubmitvalueparser) callback prop on the [`JsonTree`](https://github.com/oxyno-zeta/react-editable-json-tree/blob/09a0ca97835b0834ad054563e2fddc6f22bc5d8c/src/JsonTree.js) component should be ***unaffected***. This vulnerability exists in the default `onSubmitValueParser` prop which calls [`parse`](https://github.com/oxyno-zeta/react-editable-json-tree/blob/master/src/utils/parse.js#L30). Prop is added to `JsonTree` called `allowFunctionEvaluation`. This prop will be set to `true` in v2.2.2, which allows upgrade without losing backwards-compatibility. In v2.2.2, we switched from using `eval` to using [`Function`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Function) to construct anonymous functions. This is better than `eval` for the following reasons: – Arbitrary code should not be able to execute immediately, since the `Function` constructor explicitly *only creates* anonymous functions – Functions are created without local closures, so they only have access to the global scope If you use: – **Version `<2.2.2`**, you must upgrade as soon as possible. - **Version `^2.2.2`**, you must explicitly set `JsonTree`'s `allowFunctionEvaluation` prop to `false` to fully mitigate this vulnerability. - **Version `>=3.0.0`**, `allowFunctionEvaluation` is already set to `false` by default, so no further steps are necessary.

TensorFlow is an open source platform for machine learning. If `Requantize` is given `input_min`, `input_max`, `requested_output_min`, `requested_output_max` tensors of a nonzero rank, it results in a segfault 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.

In grails-databinding in Grails before 3.3.15, 4.x before 4.1.1, 5.x before 5.1.9, and 5.2.x before 5.2.1 (at least when certain Java 8 configurations are used), data binding allows a remote attacker to execute code by gaining access to the class loader.

TensorFlow is an open source platform for machine learning. The implementation of `BlockLSTMGradV2` does not fully validate its inputs. This results in a a segfault that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 2a458fc4866505be27c62f81474ecb2b870498fa. 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.