The paper "Safety Alignment Should Be Made More Than Just a Few Tokens Deep" by Xiangyu Qi, Ashwinee Panda, Kaifeng Lyu, Xiao Ma, Subhrajit Roy, Ahmad Beirami, Pramek Mittal, and Peter Henderson from Princeton University and Google DeepMind, addresses the vulnerabilities in current Large Language Models (LLMs) due to shallow safety alignment. The authors argue that many of these vulnerabilities stem from the fact that safety alignment primarily affects only the first few output tokens, a phenomenon they term "shallow safety alignment." They present case studies to explain why this issue exists and provide evidence that current aligned LLMs are susceptible to various attacks, including adversarial suffix attacks, prefixing attacks, decoding parameter attacks, and fine-tuning attacks. The paper highlights that shallow safety alignment can be exploited in multiple ways, such as by prefilling harmful responses with refusal prefixes, optimizing suffixes to force harmful responses, and using random sampling with appropriate decoding parameters. These attacks can lead to catastrophic failures in generating harmful content, even if the initial output tokens are safe. To address these vulnerabilities, the authors propose two main strategies: 1. **Data Augmentation with Safety Recovery Examples:** This approach involves augmenting the training data with examples that force the model to suppress harmful content more deeply within the response, thereby extending the influence of safety alignment beyond the first few tokens. 2. **Token-wise Constrained Objective for Custom Fine-tuning:** This objective function constrains the generative distribution of the first few tokens to prevent them from deviating significantly during fine-tuning, thereby maintaining the safety alignment. The authors demonstrate that these strategies can improve the robustness of LLMs against various attacks and maintain their utility in downstream tasks. They conclude that future safety alignment should be made more than just a few tokens deep to enhance the overall safety and robustness of LLMs.The paper "Safety Alignment Should Be Made More Than Just a Few Tokens Deep" by Xiangyu Qi, Ashwinee Panda, Kaifeng Lyu, Xiao Ma, Subhrajit Roy, Ahmad Beirami, Pramek Mittal, and Peter Henderson from Princeton University and Google DeepMind, addresses the vulnerabilities in current Large Language Models (LLMs) due to shallow safety alignment. The authors argue that many of these vulnerabilities stem from the fact that safety alignment primarily affects only the first few output tokens, a phenomenon they term "shallow safety alignment." They present case studies to explain why this issue exists and provide evidence that current aligned LLMs are susceptible to various attacks, including adversarial suffix attacks, prefixing attacks, decoding parameter attacks, and fine-tuning attacks. The paper highlights that shallow safety alignment can be exploited in multiple ways, such as by prefilling harmful responses with refusal prefixes, optimizing suffixes to force harmful responses, and using random sampling with appropriate decoding parameters. These attacks can lead to catastrophic failures in generating harmful content, even if the initial output tokens are safe. To address these vulnerabilities, the authors propose two main strategies: 1. **Data Augmentation with Safety Recovery Examples:** This approach involves augmenting the training data with examples that force the model to suppress harmful content more deeply within the response, thereby extending the influence of safety alignment beyond the first few tokens. 2. **Token-wise Constrained Objective for Custom Fine-tuning:** This objective function constrains the generative distribution of the first few tokens to prevent them from deviating significantly during fine-tuning, thereby maintaining the safety alignment. The authors demonstrate that these strategies can improve the robustness of LLMs against various attacks and maintain their utility in downstream tasks. They conclude that future safety alignment should be made more than just a few tokens deep to enhance the overall safety and robustness of LLMs.