Defending Against Fine-tuning Jailbreak Attack with Backdoor Enhanced Alignment

מושגי ליבה

The author proposes a novel method, Backdoor Enhanced Alignment, to defend against the Fine-tuning Jailbreak Attack by incorporating safety examples with a secret prompt. This method effectively maintains model safety alignment during fine-tuning with limited safety examples.

תקציר

The paper addresses the vulnerability of Large Language Models (LLMs) to Fine-tuning based Jailbreak Attacks (FJAttack). It introduces the Backdoor Enhanced Safety Alignment method to mitigate these attacks efficiently and effectively. The experiments demonstrate that as few as 11 prefixed safety examples can defend against FJAttack without compromising fine-tuning task performance.
Large Language Models (LLMs) like GPT-4 and Llama-2 require fine-tuning for specific business demands, but this process introduces new safety threats. The Fine-tuning based Jailbreak Attack (FJAttack) compromises model safety by incorporating harmful examples into the dataset. Existing defenses are inefficient, requiring a substantial amount of safety examples.
To address this challenge, the authors propose the Backdoor Enhanced Safety Alignment method inspired by backdoor attacks. By integrating prefixed safety examples with a secret prompt into the fine-tuning dataset, they establish a strong correlation between the trigger and safety responses. Extensive experiments show that this approach significantly reduces the Attack Success Rate under different models while preserving utility on standard tasks.
Furthermore, real-world scenarios involving dialog summary and SQL generation tasks demonstrate the effectiveness of the proposed defense method in practical settings. The results highlight how Backdoor Enhanced Alignment can enhance model robustness against FJAttacks without compromising task performance.

סטטיסטיקה

Incorporating just a few harmful examples into fine-tuning dataset can compromise model safety.
Adding as few as 11 prefixed safety examples with a secret prompt can effectively defend against FJAttack.
Our method shows efficacy in defending against FJAttack without harming fine-tuning task performance.
The proposed defense method significantly reduces Harmfulness Score and ASR compared to Baseline methods.
Secret prompt length of 150 tokens is optimal for effectiveness in defending against FJAttack.

ציטוטים

"We propose a Backdoor Enhanced Safety Alignment method inspired by an analogy with backdoor attacks."
"Our extensive experiments demonstrate that adding as few as 11 prefixed safety examples can effectively defend against FJAttack."

תובנות מפתח מזוקקות מ:

Mitigating Fine-tuning Jailbreak Attack with Backdoor Enhanced Alignment

by Jiongxiao Wa... ב- arxiv.org 02-29-2024

https://arxiv.org/pdf/2402.14968.pdf

שאלות מעמיקות

How can we extend the proposed defense method to other types of attacks on large language models?

To extend the proposed defense method to other types of attacks on large language models, we can explore incorporating similar backdoor trigger mechanisms in different attack scenarios. By designing specific secret prompts that act as triggers for safety alignment, we can potentially defend against various adversarial attacks targeting LLMs. Additionally, adapting the concept of backdoor enhanced alignment to different attack vectors and vulnerabilities in LLMs could provide a comprehensive defense strategy against a broader range of threats.

What implications does the vulnerability to FJAttacks have on the adoption of large language models in real-world applications?

The vulnerability to Fine-tuning Jailbreak Attacks (FJAttacks) poses significant risks for the adoption of large language models in real-world applications. These vulnerabilities highlight potential security concerns surrounding fine-tuning processes and data integrity when customizing LLMs for specific tasks or domains. The presence of such vulnerabilities may deter organizations from fully leveraging LLM capabilities due to concerns about compromising model safety and integrity.

How might incorporating ethical considerations impact the development and deployment of defense strategies for cyber threats like FJAttacks?

Incorporating ethical considerations into the development and deployment of defense strategies for cyber threats like FJAttacks is crucial for ensuring responsible use of technology. Ethical considerations can guide decision-making processes related to defending against such attacks by prioritizing user privacy, data security, and model transparency. By aligning defense strategies with ethical principles, developers can mitigate risks associated with malicious activities while upholding moral standards and societal values within cybersecurity practices.