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| CVE | Vendors | Products | Updated | CVSS v3.1 |
|---|---|---|---|---|
| CVE-2021-41248 | 1 Graphql | 1 Graphiql | 2024-11-21 | 7.1 High |
| GraphiQL is the reference implementation of this monorepo, GraphQL IDE, an official project under the GraphQL Foundation. All versions of graphiql older than graphiql@1.4.7 are vulnerable to compromised HTTP schema introspection responses or schema prop values with malicious GraphQL type names, exposing a dynamic XSS attack surface that can allow code injection on operation autocomplete. In order for the attack to take place, the user must load a vulnerable schema in graphiql. There are a number of ways that can occur. By default, the schema URL is not attacker-controllable in graphiql or in its suggested implementations or examples, leaving only very complex attack vectors. If a custom implementation of graphiql's fetcher allows the schema URL to be set dynamically, such as a URL query parameter like ?endpoint= in graphql-playground, or a database provided value, then this custom graphiql implementation is vulnerable to phishing attacks, and thus much more readily available, low or no privelege level xss attacks. The URLs could look like any generic looking graphql schema URL. It should be noted that desktop clients such as Altair, Insomnia, Postwoman, do not appear to be impacted by this. This vulnerability does not impact codemirror-graphql, monaco-graphql or other dependents, as it exists in onHasCompletion.ts in graphiql. It does impact all forks of graphiql, and every released version of graphiql. | ||||
| CVE-2021-41242 | 1 Frentix | 1 Openolat | 2024-11-21 | 8.1 High |
| OpenOlat is a web-basedlearning management system. A path traversal vulnerability exists in OpenOlat prior to versions 15.5.12 and 16.0.5. By providing a filename that contains a relative path as a parameter in some REST methods, it is possible to create directory structures and write files anywhere on the target system. The attack could be used to write files anywhere in the web root folder or outside, depending on the configuration of the system and the properly configured permission of the application server user. The attack requires an OpenOlat user account, an enabled REST API and the rights on a business object to call the vulnerable REST calls. The problem is fixed in version 15.5.12 and 16.0.5. There is a workaround available. The vulnerability requires the REST module to be enabled. Disabling the REST module or limiting the REST module via some firewall or web-server access rules to be accessed only be trusted systems will mitigate the risk. | ||||
| CVE-2021-41238 | 1 Hangfire | 1 Hangfire | 2024-11-21 | 8.6 High |
| Hangfire is an open source system to perform background job processing in a .NET or .NET Core applications. No Windows Service or separate process required. Dashboard UI in Hangfire.Core uses authorization filters to protect it from showing sensitive data to unauthorized users. By default when no custom authorization filters specified, `LocalRequestsOnlyAuthorizationFilter` filter is being used to allow only local requests and prohibit all the remote requests to provide sensible, protected by default settings. However due to the recent changes, in version 1.7.25 no authorization filters are used by default, allowing remote requests to succeed. If you are using `UseHangfireDashboard` method with default `DashboardOptions.Authorization` property value, then your installation is impacted. If any other authorization filter is specified in the `DashboardOptions.Authorization` property, the you are not impacted. Patched versions (1.7.26) are available both on Nuget.org and as a tagged release on the github repo. Default authorization rules now prohibit remote requests by default again by including the `LocalRequestsOnlyAuthorizationFilter` filter to the default settings. Please upgrade to the newest version in order to mitigate the issue. For users who are unable to upgrade it is possible to mitigate the issue by using the `LocalRequestsOnlyAuthorizationFilter` explicitly when configuring the Dashboard UI. | ||||
| CVE-2021-41232 | 1 Thunderdome | 1 Planning Poker | 2024-11-21 | 8.1 High |
| Thunderdome is an open source agile planning poker tool in the theme of Battling for points. In affected versions there is an LDAP injection vulnerability which affects instances with LDAP authentication enabled. The provided username is not properly escaped. This issue has been patched in version 1.16.3. If users are unable to update they should disable the LDAP feature if in use. | ||||
| CVE-2021-41228 | 1 Google | 1 Tensorflow | 2024-11-21 | 7.5 High |
| TensorFlow is an open source platform for machine learning. In affected versions TensorFlow's `saved_model_cli` tool is vulnerable to a code injection as it calls `eval` on user supplied strings. This can be used by attackers to run arbitrary code on the plaform where the CLI tool runs. However, given that the tool is always run manually, the impact of this is not severe. We have patched this by adding a `safe` flag which defaults to `True` and an explicit warning for users. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | ||||
| CVE-2021-41226 | 1 Google | 1 Tensorflow | 2024-11-21 | 7.1 High |
| TensorFlow is an open source platform for machine learning. In affected versions the implementation of `SparseBinCount` is vulnerable to a heap OOB access. This is because of missing validation between the elements of the `values` argument and the shape of the sparse output. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | ||||
| CVE-2021-41224 | 1 Google | 1 Tensorflow | 2024-11-21 | 7.1 High |
| TensorFlow is an open source platform for machine learning. In affected versions the implementation of `SparseFillEmptyRows` can be made to trigger a heap OOB access. This occurs whenever the size of `indices` does not match the size of `values`. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | ||||
| CVE-2021-41223 | 1 Google | 1 Tensorflow | 2024-11-21 | 7.1 High |
| TensorFlow is an open source platform for machine learning. In affected versions the implementation of `FusedBatchNorm` kernels is vulnerable to a heap OOB access. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | ||||
| CVE-2021-41221 | 1 Google | 1 Tensorflow | 2024-11-21 | 7.8 High |
| TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for the `Cudnn*` operations in TensorFlow can be tricked into accessing invalid memory, via a heap buffer overflow. This occurs because the ranks of the `input`, `input_h` and `input_c` parameters are not validated, but code assumes they have certain values. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | ||||
| CVE-2021-41220 | 1 Google | 1 Tensorflow | 2024-11-21 | 7.8 High |
| TensorFlow is an open source platform for machine learning. In affected versions the async implementation of `CollectiveReduceV2` suffers from a memory leak and a use after free. This occurs due to the asynchronous computation and the fact that objects that have been `std::move()`d from are still accessed. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, as this version is the only one that is also affected. | ||||
| CVE-2021-41219 | 1 Google | 1 Tensorflow | 2024-11-21 | 7.8 High |
| TensorFlow is an open source platform for machine learning. In affected versions the code for sparse matrix multiplication is vulnerable to undefined behavior via binding a reference to `nullptr`. This occurs whenever the dimensions of `a` or `b` are 0 or less. In the case on one of these is 0, an empty output tensor should be allocated (to conserve the invariant that output tensors are always allocated when the operation is successful) but nothing should be written to it (that is, we should return early from the kernel implementation). Otherwise, attempts to write to this empty tensor would result in heap OOB access. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | ||||
| CVE-2021-41214 | 1 Google | 1 Tensorflow | 2024-11-21 | 7.8 High |
| TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for `tf.ragged.cross` has an undefined behavior due to binding a reference to `nullptr`. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | ||||
| CVE-2021-41212 | 1 Google | 1 Tensorflow | 2024-11-21 | 7.1 High |
| TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for `tf.ragged.cross` can trigger a read outside of bounds of heap allocated array. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | ||||
| CVE-2021-41211 | 1 Google | 1 Tensorflow | 2024-11-21 | 7.1 High |
| TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for `QuantizeV2` can trigger a read outside of bounds of heap allocated array. This occurs whenever `axis` is a negative value less than `-1`. In this case, we are accessing data before the start of a heap buffer. The code allows `axis` to be an optional argument (`s` would contain an `error::NOT_FOUND` error code). Otherwise, it assumes that `axis` is a valid index into the dimensions of the `input` tensor. If `axis` is less than `-1` then this results in a heap OOB read. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, as this version is the only one that is also affected. | ||||
| CVE-2021-41210 | 1 Google | 1 Tensorflow | 2024-11-21 | 7.1 High |
| TensorFlow is an open source platform for machine learning. In affected versions the shape inference functions for `SparseCountSparseOutput` can trigger a read outside of bounds of heap allocated array. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | ||||
| CVE-2021-41208 | 1 Google | 1 Tensorflow | 2024-11-21 | 8.8 High |
| TensorFlow is an open source platform for machine learning. In affected versions the code for boosted trees in TensorFlow is still missing validation. As a result, attackers can trigger denial of service (via dereferencing `nullptr`s or via `CHECK`-failures) as well as abuse undefined behavior (binding references to `nullptr`s). An attacker can also read and write from heap buffers, depending on the API that gets used and the arguments that are passed to the call. Given that the boosted trees implementation in TensorFlow is unmaintained, it is recommend to no longer use these APIs. We will deprecate TensorFlow's boosted trees APIs in subsequent releases. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | ||||
| CVE-2021-41206 | 1 Google | 1 Tensorflow | 2024-11-21 | 7 High |
| TensorFlow is an open source platform for machine learning. In affected versions several TensorFlow operations are missing validation for the shapes of the tensor arguments involved in the call. Depending on the API, this can result in undefined behavior and segfault or `CHECK`-fail related crashes but in some scenarios writes and reads from heap populated arrays are also possible. We have discovered these issues internally via tooling while working on improving/testing GPU op determinism. As such, we don't have reproducers and there will be multiple fixes for these issues. These fixes will be included in TensorFlow 2.7.0. We will also cherrypick these commits on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | ||||
| CVE-2021-41205 | 1 Google | 1 Tensorflow | 2024-11-21 | 7.1 High |
| TensorFlow is an open source platform for machine learning. In affected versions the shape inference functions for the `QuantizeAndDequantizeV*` operations can trigger a read outside of bounds of heap allocated array. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | ||||
| CVE-2021-41203 | 1 Google | 1 Tensorflow | 2024-11-21 | 7.8 High |
| TensorFlow is an open source platform for machine learning. In affected versions an attacker can trigger undefined behavior, integer overflows, segfaults and `CHECK`-fail crashes if they can change saved checkpoints from outside of TensorFlow. This is because the checkpoints loading infrastructure is missing validation for invalid file formats. The fixes will be included in TensorFlow 2.7.0. We will also cherrypick these commits on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | ||||
| CVE-2021-41201 | 1 Google | 1 Tensorflow | 2024-11-21 | 7.8 High |
| TensorFlow is an open source platform for machine learning. In affeced versions during execution, `EinsumHelper::ParseEquation()` is supposed to set the flags in `input_has_ellipsis` vector and `*output_has_ellipsis` boolean to indicate whether there is ellipsis in the corresponding inputs and output. However, the code only changes these flags to `true` and never assigns `false`. This results in unitialized variable access if callers assume that `EinsumHelper::ParseEquation()` always sets these flags. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | ||||