ghsa

### Impact Users of the MLflow Open Source Project who are hosting the MLflow Model Registry using the `mlflow server` or `mlflow ui` commands using an MLflow version older than MLflow 2.2.1 may be vulnerable to a remote file existence check exploit if they are not limiting who can query their server (for example, by using a cloud VPC, an IP allowlist for inbound requests, or authentication / authorization middleware). This issue only affects users and integrations that run the `mlflow server` and `mlflow ui` commands. Integrations that do not make use of `mlflow server` or `mlflow ui` are unaffected; for example, the Databricks Managed MLflow product and MLflow on Azure Machine Learning do not make use of these commands and are not impacted by these vulnerabilities in any way. The vulnerability detailed in https://nvd.nist.gov/vuln/detail/CVE-2023-1176 enables an actor to check the existence of arbitrary files unrelated to MLflow from the host server, including any files stored in ...

### Impact Users of the MLflow Open Source Project who are hosting the MLflow Model Registry using the `mlflow server` or `mlflow ui` commands using an MLflow version older than MLflow 2.2.1 may be vulnerable to a remote file access exploit if they are not limiting who can query their server (for example, by using a cloud VPC, an IP allowlist for inbound requests, or authentication / authorization middleware). This issue only affects users and integrations that run the `mlflow server` and `mlflow ui` commands. Integrations that do not make use of `mlflow server` or `mlflow ui` are unaffected; for example, the Databricks Managed MLflow product and MLflow on Azure Machine Learning do not make use of these commands and are not impacted by these vulnerabilities in any way. The vulnerability detailed in https://nvd.nist.gov/vuln/detail/CVE-2023-1177 enables an actor to download arbitrary files unrelated to MLflow from the host server, including any files stored in remote locations to whi...

### Impact An issue was discovered in the `Versionize::deserialize` implementation provided by the `versionize` crate for `vmm_sys_util::fam::FamStructWrapper`, which can lead to out of bounds memory accesses. ### Patches The impact started with version 0.1.1. The issue was corrected in version 0.1.10 by inserting a check that verifies, for any deserialized header, the lengths of compared flexible arrays are equal and aborting deserialization otherwise. ### Workarounds \- ### References - https://github.com/firecracker-microvm/versionize/pull/53

The NATS official Rust clients are vulnerable to MitM when using TLS. The common name of the server's TLS certificate is validated against the `host`name provided by the server's plaintext `INFO` message during the initial connection setup phase. A MitM proxy can tamper with the `host` field's value by substituting it with the common name of a valid certificate it controls, fooling the client into accepting it. ## Reproduction steps 1. The NATS Rust client tries to establish a new connection 2. The connection is intercepted by a MitM proxy 3. The proxy makes a separate connection to the NATS server 4. The NATS server replies with an `INFO` message 5. The proxy reads the `INFO`, alters the `host` JSON field and passes the tampered `INFO` back to the client 6. The proxy upgrades the client connection to TLS, presenting a certificate issued by a certificate authority present in the client's keychain. In the previous step the `host` was set to the common name of said certificate 7. `rus...

### Impact If the parameter `indices` for `DynamicStitch` does not match the shape of the parameter `data`, it can trigger an stack OOB read. ```python import tensorflow as tf func = tf.raw_ops.DynamicStitch para={'indices': [[0xdeadbeef], [405], [519], [758], [1015]], 'data': [[110.27793884277344], [120.29475402832031], [157.2418212890625], [157.2626953125], [188.45382690429688]]} y = func(**para) ``` ### Patches We have patched the issue in GitHub commit [ee004b18b976eeb5a758020af8880236cd707d05](https://github.com/tensorflow/tensorflow/commit/ee004b18b976eeb5a758020af8880236cd707d05). The fix will be included in TensorFlow 2.12. We will also cherrypick this commit on TensorFlow 2.11.1. ### For more information Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions. ### Attribution This has been reported via Google OSS VRP.

### Impact When the parameter `summarize` of `tf.raw_ops.Print` is zero, the new method `SummarizeArray<bool>` will reference to a nullptr, leading to a seg fault. ```python import tensorflow as tf tf.raw_ops.Print(input = tf.constant([1, 1, 1, 1],dtype=tf.int32), data = [[False, False, False, False], [False], [False, False, False]], message = 'tmp/I', first_n = 100, summarize = 0) ``` ### Patches We have patched the issue in GitHub commit [6d423b8bcc9aa9f5554dc988c1c16d038b508df1](https://github.com/tensorflow/tensorflow/commit/6d423b8bcc9aa9f5554dc988c1c16d038b508df1). The fix will be included in TensorFlow 2.12. We will also cherrypick this commit on TensorFlow 2.11.1. ### For more information Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us...

### Impact TFversion 2.11.0 //tensorflow/core/ops/array_ops.cc:1067 const Tensor* hypothesis_shape_t = c->input_tensor(2); std::vector<DimensionHandle> dims(hypothesis_shape_t->NumElements() - 1); for (int i = 0; i < dims.size(); ++i) { dims[i] = c->MakeDim(std::max(h_values(i), t_values(i))); } if hypothesis_shape_t is empty, hypothesis_shape_t->NumElements() - 1 will be integer overflow, and the it will deadlock ```python import tensorflow as tf para={ 'hypothesis_indices': [[]], 'hypothesis_values': ['tmp/'], 'hypothesis_shape': [], 'truth_indices': [[]], 'truth_values': [''], 'truth_shape': [], 'normalize': False } tf.raw_ops.EditDistance(**para) ``` ### Patches We have patched the issue in GitHub commit [08b8e18643d6dcde00890733b270ff8d9960c56c](https://github.com/tensorflow/tensorflow/commit/08b8e18643d6dcde00890733b270ff8d9960c56c). The fix will be included in TensorFlow 2.12.0. We will also cherrypick this commit on TensorFlow 2.11.1 ### For...

### Impact When ctx->step_containter() is a null ptr, the Lookup function will be executed with a null pointer. ```python import tensorflow as tf tf.raw_ops.TensorArrayConcatV2(handle=['a', 'b'], flow_in = 0.1, dtype=tf.int32, element_shape_except0=1) ``` ### Patches We have patched the issue in GitHub commit [239139d2ae6a81ae9ba499ad78b56d9b2931538a](https://github.com/tensorflow/tensorflow/commit/239139d2ae6a81ae9ba499ad78b56d9b2931538a). The fix will be included in TensorFlow 2.12.0. We will also cherrypick this commit on TensorFlow 2.11.1 ### For more information Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions. ### Attribution This vulnerability has been reported by Yu Tian

### Impact ```python import os os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0' import tensorflow as tf print(tf.__version__) with tf.device("CPU"): ksize = [1, 40, 128, 1] strides = [1, 128, 128, 30] padding = "SAME" data_format = "NHWC" orig_input_shape = [11, 9, 78, 9] grad = tf.saturate_cast(tf.random.uniform([16, 16, 16, 16], minval=-128, maxval=129, dtype=tf.int64), dtype=tf.float32) res = tf.raw_ops.AvgPoolGrad( ksize=ksize, strides=strides, padding=padding, data_format=data_format, orig_input_shape=orig_input_shape, grad=grad, ) ``` ### Patches We have patched the issue in GitHub commit [ddaac2bdd099bec5d7923dea45276a7558217e5b](https://github.com/tensorflow/tensorflow/commit/ddaac2bdd099bec5d7923dea45276a7558217e5b). The fix will be included in TensorFlow 2.12.0. We will also cherrypick this commit on TensorFlow 2.11.1 ### For more information Please consult [our security guide](https://github.com/ten...

### Impact When `SparseSparseMaximum` is given invalid sparse tensors as inputs, it can give an NPE. ```python import tensorflow as tf tf.raw_ops.SparseSparseMaximum( a_indices=[[1]], a_values =[ 0.1 ], a_shape = [2], b_indices=[[]], b_values =[2 ], b_shape = [2], ) ``` ### Patches We have patched the issue in GitHub commit [5e0ecfb42f5f65629fd7a4edd6c4afe7ff0feb04](https://github.com/tensorflow/tensorflow/commit/5e0ecfb42f5f65629fd7a4edd6c4afe7ff0feb04). The fix will be included in TensorFlow 2.12. We will also cherrypick this commit on TensorFlow 2.11.1. ### For more information Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions. ### Attribution This vulnerability has been reported by Yu Tian of Qihoo 360 AIVul Team