Backdoor Attacks on Large Language Models: Challenges and Innovations with Contrastive Knowledge Distillation

Główne pojęcia

Backdoor attacks can be effectively transferred from small-scale teacher models to large-scale student models through contrastive knowledge distillation, overcoming the challenges of implementing backdoor attacks on parameter-efficient fine-tuning.

Streszczenie

This paper explores the effectiveness of backdoor attacks targeting parameter-efficient fine-tuning (PEFT) algorithms for large language models (LLMs). The authors first validate that compared to full-parameter fine-tuning, clean label backdoor attacks targeting PEFT may struggle to establish alignment between triggers and target labels, hindering the achievement of feasible attack success rates.

To address this issue, the authors propose a novel backdoor attack algorithm called W2SAttack (Weak-to-Strong Attack) based on contrastive knowledge distillation. The key idea is to first poison a small-scale teacher model through full-parameter fine-tuning to embed backdoor functionality. This poisoned teacher model then covertly transfers the backdoor to a large-scale student model using contrastive knowledge distillation, which aligns the student model's trigger feature representations with the teacher's.

The authors demonstrate the superior performance of W2SAttack across various settings, including different language models, backdoor attack algorithms, and teacher model architectures. Experimental results show that W2SAttack can achieve attack success rates close to 100% when targeting PEFT, while maintaining the classification performance of the model. The authors also provide theoretical analysis based on information bottleneck theory to explain the challenges of backdoor attacks on PEFT.

Statystyki

"Compared to full-parameter fine-tuning, the attack success rate significantly drops under a PEFT method LoRA, for example decreasing from 99.23% to 15.51% for BadNet."
"When targeting full-parameter fine-tuning, the attack success rate is nearly 100%. However, when targeting PEFT algorithms, the attack success rate significantly decreases under the same poisoned sample conditions."
"Using our W2SAttack algorithm, the average attack success rate increases by 58.48% on the SST-2 dataset compared to LoRA."

Cytaty

"Backdoor attacks aim to implant backdoors into LLMs through fine-tuning."
"To reduce the cost of fine-tuning, Parameter-Efficient Fine-Tuning (PEFT) is proposed, but we find that PEFT cannot fulfill backdoor attacks."
"From an innovative perspective, we introduce a novel backdoor attack algorithm that utilizes the weak language model to propagate backdoor features to strong LLMs through contrastive knowledge distillation."

Kluczowe wnioski z

Weak-To-Strong Backdoor Attacks for LLMs with Contrastive Knowledge Distillation

by Shuai Zhao, ... o arxiv.org 09-27-2024

https://arxiv.org/pdf/2409.17946.pdf

Głębsze pytania

How can the W2SAttack algorithm be extended to defend against other types of attacks beyond backdoor attacks, such as adversarial attacks or jailbreak attacks?

The W2SAttack algorithm, primarily designed for enhancing backdoor attacks in parameter-efficient fine-tuning (PEFT) settings, can be adapted to defend against other types of attacks, including adversarial attacks and jailbreak attacks, by leveraging its core principles of knowledge distillation and feature alignment.

Adversarial Attack Defense: The contrastive knowledge distillation approach can be modified to include adversarial training techniques. By incorporating adversarial examples into the training process of the student model, the algorithm can learn to recognize and mitigate the effects of adversarial perturbations. This could involve generating adversarial samples during the distillation process and training the student model to maintain its performance on both clean and adversarial inputs. The alignment of features between the teacher and student models can be enhanced to ensure that the student model is robust against adversarial manipulations.

Jailbreak Attack Mitigation: For jailbreak attacks, which exploit vulnerabilities in language models to bypass safety mechanisms, W2SAttack can be extended by integrating a mechanism that identifies and filters out potentially harmful inputs. The teacher model can be trained on a dataset that includes both benign and malicious prompts, allowing it to learn the distinctions between safe and unsafe queries. The student model can then be fine-tuned to reject or flag inputs that resemble those identified as jailbreak attempts, thereby enhancing the model's security posture.

Generalized Feature Learning: The underlying principle of transferring knowledge from a smaller, poisoned teacher model to a larger student model can be applied to various attack scenarios. By training the teacher model on diverse attack types, the student model can learn to generalize its defenses across multiple attack vectors, making it more resilient to a broader range of threats.

What are the potential ethical concerns and societal implications of developing more effective backdoor attack techniques, even if the intent is to improve model security?

The development of more effective backdoor attack techniques, even with the intent to enhance model security, raises several ethical concerns and societal implications:

Misuse of Technology: Enhanced backdoor attack techniques could be exploited by malicious actors to compromise systems, leading to unauthorized access, data breaches, and manipulation of AI systems. The dual-use nature of such technologies poses significant risks, as they can be employed for both defensive and offensive purposes.

Trust and Transparency: The existence of effective backdoor attacks can undermine trust in AI systems. Users and stakeholders may become wary of deploying AI models if they fear that these models could be secretly manipulated. This erosion of trust can hinder the adoption of AI technologies across various sectors, including healthcare, finance, and public safety.

Regulatory Challenges: As backdoor attacks become more sophisticated, regulatory bodies may struggle to keep pace with the evolving landscape of AI security threats. This could lead to gaps in legislation and oversight, allowing malicious activities to proliferate without adequate checks and balances.

Ethical AI Development: The pursuit of more effective backdoor techniques raises questions about the ethical responsibilities of researchers and developers. There is a need for a framework that balances innovation with ethical considerations, ensuring that advancements in AI security do not inadvertently contribute to harm.

Societal Impact: The societal implications of deploying models vulnerable to backdoor attacks can be profound. For instance, if AI systems used in critical infrastructure are compromised, the consequences could be catastrophic, affecting public safety and national security. Therefore, the ethical development of AI technologies must prioritize the minimization of risks associated with backdoor vulnerabilities.

Could the contrastive knowledge distillation approach used in W2SAttack be applied to other domains beyond language models, such as computer vision or robotics, to enhance the transfer of desired behaviors or features?

Yes, the contrastive knowledge distillation approach utilized in W2SAttack can be effectively applied to other domains beyond language models, including computer vision and robotics, to enhance the transfer of desired behaviors or features. Here’s how it can be implemented in these domains:

Computer Vision: In computer vision tasks, contrastive knowledge distillation can be employed to transfer learned features from a teacher model (which may be trained on a diverse dataset) to a student model that is more lightweight or specialized. For instance, a teacher model could be trained to recognize a wide variety of objects, while the student model could be fine-tuned to focus on a specific subset of those objects. By minimizing the distance between the feature representations of the teacher and student models, the student can learn to generalize better and improve its performance on specific tasks, such as object detection or image classification.

Robotics: In robotics, the contrastive knowledge distillation approach can facilitate the transfer of complex behaviors from a teacher robot (or simulation) to a student robot. For example, a teacher robot trained in a simulated environment can demonstrate various tasks, such as navigation or manipulation. The student robot can then learn to replicate these behaviors by aligning its feature representations with those of the teacher. This method can enhance the efficiency of training in real-world scenarios, where data collection can be expensive and time-consuming.

Multi-Modal Learning: The principles of contrastive knowledge distillation can also be extended to multi-modal learning, where models learn from different types of data (e.g., images, text, and audio). By aligning features across modalities, models can develop a more comprehensive understanding of the data, leading to improved performance in tasks that require integration of information from multiple sources.

Generalization and Robustness: The approach can enhance the robustness of models in various domains by allowing them to learn from diverse examples and scenarios. This is particularly valuable in fields like autonomous driving, where models must adapt to a wide range of environmental conditions and unexpected situations.

In summary, the contrastive knowledge distillation approach from W2SAttack has the potential to significantly enhance the performance and robustness of models across various domains, making it a versatile tool in the advancement of AI technologies.