Students have taken on the burden of their teachers in the world of artificial intelligence as more organizations turn to distilled models for cost savings, faster inference, and better operational efficiency. However, despite the benefits of distilled models, they also inherit many security risks from their teacher models, as well as some new risks of their own.
One of the main concerns with distilled models is that they inherit a significant portion of their teacher model’s behavior, including any security risks that were embedded in the training data. This means that risks such as intellectual property theft, privacy leaks, and model inversion attacks could be passed down to the student models.
According to Brauchler, who is an expert in the field, typical model distillation involves using the same training data that was originally consumed by the larger teacher model, as well as the teacher model’s predictions of valid possible outputs. This process allows the student model to memorize many of the same behaviors as the teacher model, which could potentially include sensitive data from the training sets.
The security risks associated with distilled models are concerning for organizations that rely on artificial intelligence for various applications. Intellectual property theft, in particular, could have serious repercussions for businesses that invest heavily in AI technology. Privacy leaks are also a significant concern, as sensitive data could be exposed if not properly protected in distilled models.
Model inversion attacks are another risk that organizations need to consider when using distilled models. These attacks involve recovering sensitive information from a model by querying it with specifically crafted inputs. This could potentially lead to a breach of sensitive information, putting both businesses and individuals at risk.
In order to address these security risks, organizations must take steps to carefully review their training data and ensure that any sensitive information is properly protected. They should also consider implementing additional security measures, such as encryption and access controls, to protect against potential threats.
Overall, while distilled models offer many benefits in terms of cost savings and operational efficiency, organizations must be aware of the security risks that come with using these models. By taking proactive steps to mitigate these risks, organizations can continue to leverage the power of artificial intelligence while protecting their sensitive data and intellectual property.