1. When MindSpore is used for AI model training, if the user-defined computational graph structure (for example, Python code for generating the MindSpore computational graph) is provided by an untrusted third party, malicious code may exist and will be loaded and executed to attack the system.
2. Model files are stored in binary mode. When MindSpore is used to optimize or infer AI models and the model files are loaded in deserialization mode, once malicious code is written into the model files, the code are loaded and executed, causing attacks on the system.
3. MindSpore performs only model training and inference based on the data provided by users. Users need to protect data security to avoid privacy leakage.
4. MindSpore is a distributed training platform. When MindSpore is used for distributed training, if an Ascend chip is used for training, a device provides a secure transmission protocol for gradient fusion. If GPUs or other clusters are used for training, identity authentication and secure transmission are not provided.

1. Run MindSpore in the sandbox.
2. Run MindSpore as a non-root user.
3. Ensure that the source of a computational graph structure is trustworthy. Do not write code irrelevant to model training in the network structure definition.
4. Ensure that the source of a network model is trustworthy or enter secure network model parameters to prevent model parameters from being tampered with.
5. Ensure that GPU distributed training is performed on an isolated cluster network.

When run a model using MindSpore Lite, the value from the model will be read and used as the parameter or input of a operator, if the value read from the model is invalid, it may cause unexpected result. For example, if the invalid value is used as the offset of a vector, it may cause your app run into segmentation fault issue.

1. Make sure your model is well verified and protected.
2. The exception catching mechanism of C++ is an effective method to improve robustness of your app, consider adding code to catch exception when calling the MindSpore Lite API, as exception will be raised in some case such as the example mentioned in the risk description above.