What are the best practices for protecting NLP models from attacks?

3 Adversarial examples

Adversarial examples are a type of attack that aims to fool or mislead the NLP model by crafting input samples that are slightly modified or perturbed, but still look natural or legitimate to human eyes. For example, an attacker can change some words, characters, or punctuation marks in a text, or add some background noise or distortion to a speech, to cause the model to produce wrong or harmful output. To prevent adversarial examples, you should use robust and diverse training data, apply data augmentation and regularization techniques, and detect and reject suspicious input.

4 Membership inference

Membership inference is a type of attack that aims to reveal the sensitive or private information of the individuals whose data was used to train the NLP model. For example, an attacker can use a shadow or an attack model to estimate the probability that a given input sample belongs to the training data or not, and then infer some attributes or characteristics of the data owner. To prevent membership inference, you should use differential privacy or federated learning techniques, apply data anonymization and aggregation methods, and reduce the model overfitting and memorization.

5 Model inversion

Model inversion is a type of attack that aims to reconstruct or recover the original input data or some features of it from the NLP model output or parameters. For example, an attacker can use gradient-based or optimization-based methods to generate input samples that maximize the model output or activation, and then compare them with the real data. To prevent model inversion, you should use homomorphic encryption or secure multi-party computation techniques, apply data compression and transformation methods, and limit the model complexity and expressiveness.

6 Backdoor attacks

Backdoor attacks are a type of attack that aims to implant a hidden or latent functionality into the NLP model that can be triggered by a specific input pattern or condition. For example, an attacker can insert a malicious code or a trigger word into the model during the training or deployment phase, and then activate it later to cause the model to behave abnormally or maliciously. To prevent backdoor attacks, you should use secure and reliable model sources, apply model verification and validation techniques, and monitor the model behavior and anomalies.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?