Representation Noising (RepNoise) is a defense mechanism designed to prevent harmful fine-tuning attacks (HFAs) on large language models (LLMs). The method works by removing information about harmful representations from the model's internal layers, making it difficult for attackers to recover these representations during fine-tuning. This approach is effective even when attackers have access to the model's weights and the defender has no control over the model after the attack. RepNoise generalizes across different harmful tasks and does not degrade the model's ability to perform harmless tasks. The effectiveness of RepNoise is attributed to its "depth," which refers to the extent to which harmful information is removed across all layers of the LLM. The paper introduces four immunization conditions that a defense must meet to be effective against HFAs: resistance, stability, generalization, and trainability. RepNoise satisfies all these conditions, as demonstrated through extensive experiments. The method is evaluated on two tasks: harmful question-answering and toxic content generation. Results show that RepNoise significantly reduces the harmfulness of models compared to other defense mechanisms, including adversarial loss and security vectors. It also maintains the model's ability to perform on harmless tasks and generalizes well to unseen harmful tasks. The method is based on minimizing the mutual information between inputs and representations, as well as between outputs and representations. This is achieved through a combination of adversarial loss and noise loss, which encourages the model to learn representations that are less informative about harmful tasks. The paper also provides a mechanistic analysis of RepNoise, showing that it effectively removes harmful information across all layers of the model. This is supported by experiments that measure the differences in model weights and token probabilities before and after the application of RepNoise. The study concludes that RepNoise is an effective defense against HFAs, as it meets all the immunization criteria and maintains the model's ability to perform on harmless tasks. The method is also shown to be generalizable across different harmful tasks and is not affected by variations in hyperparameter choices. However, the paper acknowledges that RepNoise may not be foolproof, as it can be defeated with higher learning rates and more data. Future work should focus on improving cross-domain defense and exploring other modalities for LLMs.Representation Noising (RepNoise) is a defense mechanism designed to prevent harmful fine-tuning attacks (HFAs) on large language models (LLMs). The method works by removing information about harmful representations from the model's internal layers, making it difficult for attackers to recover these representations during fine-tuning. This approach is effective even when attackers have access to the model's weights and the defender has no control over the model after the attack. RepNoise generalizes across different harmful tasks and does not degrade the model's ability to perform harmless tasks. The effectiveness of RepNoise is attributed to its "depth," which refers to the extent to which harmful information is removed across all layers of the LLM. The paper introduces four immunization conditions that a defense must meet to be effective against HFAs: resistance, stability, generalization, and trainability. RepNoise satisfies all these conditions, as demonstrated through extensive experiments. The method is evaluated on two tasks: harmful question-answering and toxic content generation. Results show that RepNoise significantly reduces the harmfulness of models compared to other defense mechanisms, including adversarial loss and security vectors. It also maintains the model's ability to perform on harmless tasks and generalizes well to unseen harmful tasks. The method is based on minimizing the mutual information between inputs and representations, as well as between outputs and representations. This is achieved through a combination of adversarial loss and noise loss, which encourages the model to learn representations that are less informative about harmful tasks. The paper also provides a mechanistic analysis of RepNoise, showing that it effectively removes harmful information across all layers of the model. This is supported by experiments that measure the differences in model weights and token probabilities before and after the application of RepNoise. The study concludes that RepNoise is an effective defense against HFAs, as it meets all the immunization criteria and maintains the model's ability to perform on harmless tasks. The method is also shown to be generalizable across different harmful tasks and is not affected by variations in hyperparameter choices. However, the paper acknowledges that RepNoise may not be foolproof, as it can be defeated with higher learning rates and more data. Future work should focus on improving cross-domain defense and exploring other modalities for LLMs.