wawasan - Large language model security - # Jailbreaking Attacks on Fine-Tuned and Quantized LLMs

Increased Vulnerabilities of Large Language Models Due to Fine-Tuning and Quantization

Q: How can the fine-tuning process be modified to maintain the safety alignment of LLMs?

The fine-tuning process can be modified in several ways to ensure the safety alignment of Large Language Models (LLMs). One approach is to incorporate safety constraints or objectives during the fine-tuning phase. By including specific safety-related tasks or prompts in the fine-tuning data, the model can learn to prioritize safety considerations while adapting to new tasks or domains. This can help reinforce the safety alignment established during pre-training and alignment training. Another strategy is to implement continual learning techniques during fine-tuning. Instead of fine-tuning the model on a single task or dataset, a continual learning approach involves periodically revisiting previous tasks or safety protocols to prevent catastrophic forgetting. By interleaving safety-related tasks with new training data, the model can retain its safety alignment while adapting to new information. Additionally, fine-tuning processes can be augmented with adversarial training or robust optimization techniques. By exposing the model to adversarial examples or challenging inputs during fine-tuning, it can learn to resist malicious attacks and maintain its safety alignment in the face of potential vulnerabilities. This adversarial fine-tuning can help improve the robustness of the model against jailbreaking attempts and other security threats.

Q: What other techniques, besides guardrails, can be employed to mitigate the vulnerabilities introduced by fine-tuning and quantization?

In addition to guardrails, several other techniques can be employed to mitigate the vulnerabilities introduced by fine-tuning and quantization in Large Language Models (LLMs). One approach is to implement model monitoring and anomaly detection systems that can flag unusual or potentially harmful behavior in the model's outputs. By continuously monitoring the model's responses and comparing them to expected norms, developers can quickly identify and address any deviations that may indicate security vulnerabilities. Another technique is to incorporate ensemble methods or model diversity in LLM deployment. By using multiple diverse models or versions of the same model, developers can reduce the impact of vulnerabilities introduced by fine-tuning or quantization. Ensemble methods combine the predictions of multiple models to make more robust and reliable decisions, mitigating the risks associated with individual model vulnerabilities. Furthermore, secure federated learning techniques can be utilized to train LLMs on distributed data sources without compromising data privacy or security. By leveraging federated learning frameworks, models can be trained collaboratively across multiple devices or servers while ensuring that sensitive information remains protected. This approach can help mitigate the risks of privacy leakage attacks that may arise from fine-tuning or quantization processes.

Q: How can the insights from this research be applied to ensure the responsible development and deployment of large language models in real-world applications?

The insights from this research can be applied to ensure the responsible development and deployment of Large Language Models (LLMs) in real-world applications by incorporating safety considerations at every stage of the model lifecycle. Firstly, developers should prioritize safety and security during the design, training, and fine-tuning phases of LLM development. By integrating safety protocols, alignment training, and guardrails from the outset, models can be better equipped to resist adversarial attacks and maintain ethical standards. Secondly, continuous monitoring and evaluation of LLMs in production environments are essential to detect and address vulnerabilities promptly. By implementing robust testing frameworks, anomaly detection systems, and model validation procedures, developers can proactively identify and mitigate potential risks before they escalate. Moreover, collaboration with domain experts, ethicists, and stakeholders is crucial to ensure that LLMs are deployed responsibly and ethically. By engaging in interdisciplinary discussions and incorporating diverse perspectives, developers can address societal concerns, ethical dilemmas, and potential biases that may arise from LLM deployment. Overall, by applying the insights from this research to real-world applications, developers can promote the safe, reliable, and ethical use of LLMs, fostering trust and transparency in AI technologies.

Konsep Inti

Fine-tuning and quantization of large language models (LLMs) can significantly reduce their jailbreak resistance, leading to increased vulnerabilities.

Abstrak

The paper examines the impact of downstream tasks such as fine-tuning and quantization on the vulnerability of large language models (LLMs) to jailbreaking attacks. The authors use the state-of-the-art Tree-of-attacks pruning (TAP) algorithm to test the jailbreak resistance of various foundation models (Llama2, Mistral, MosaicML) and their fine-tuned versions (CodeLlama, SQLCoder, Dolphin, Intel Neural Chat).

The results show that fine-tuning and quantization can reduce the jailbreak resistance of LLMs considerably, making them more vulnerable to attacks. For example, the jailbreak success rate increases from 6% for the Llama2-7B model to 32% for its fine-tuned version, CodeLlama-7B, and further to 82% for the SQLCoder-2 model, which is fine-tuned on top of CodeLlama-7B.

The authors also demonstrate the effectiveness of external guardrails in mitigating these vulnerabilities. By using a custom-trained jailbreak attack detector, they are able to reduce the jailbreak success rate by a factor of 9x for the Llama2-7B model and 16x for the CodeLlama-7B model.

The paper highlights the importance of incorporating safety protocols during the fine-tuning process and the need to integrate guardrails as a standard practice in responsible AI development. This approach can help ensure that the advancements in large language models prioritize both innovation and security, fostering a secure digital future.

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arxiv.org

Statistik

Llama2-7B has a jailbreak success rate of 6%.
CodeLlama-7B has a jailbreak success rate of 32%.
SQLCoder-2 has a jailbreak success rate of 82%.
Mistral-7B-v0.1 has a jailbreak success rate of 85.3%.
Dolphin-2.2.1-Mistral-7B-v0.1 has a jailbreak success rate of 99%.
MPT-7B has a jailbreak success rate of 93%.
IntelNeuralChat-7B has a jailbreak success rate of 94%.
Llama-2-7B-Chat-GGUF-8bit has a jailbreak success rate of 9%.
CodeLlama-7B-GGUF-8bit has a jailbreak success rate of 72%.
Mistral-7B-v0.1-GGUF-8bit has a jailbreak success rate of 96%.

Kutipan

"Fine-tuning or quantizing model weights alters the risk profile of LLMs, potentially undermining the safety alignment established through RLHF."
"The lack of safety measures in these fine-tuned and quantized models is concerning, highlighting the need to incorporate safety protocols during the fine-tuning process."
"The effectiveness of guardrails in preventing jailbreaking highlights the importance of integrating them with safety practices in AI development."

Wawasan Utama Disaring Dari

Increased LLM Vulnerabilities from Fine-tuning and Quantization

by Divyanshu Ku... pada arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.04392.pdf

Increased LLM Vulnerabilities from Fine-tuning and Quantization

Pertanyaan yang Lebih Dalam

How can the fine-tuning process be modified to maintain the safety alignment of LLMs?

The fine-tuning process can be modified in several ways to ensure the safety alignment of Large Language Models (LLMs). One approach is to incorporate safety constraints or objectives during the fine-tuning phase. By including specific safety-related tasks or prompts in the fine-tuning data, the model can learn to prioritize safety considerations while adapting to new tasks or domains. This can help reinforce the safety alignment established during pre-training and alignment training.
Another strategy is to implement continual learning techniques during fine-tuning. Instead of fine-tuning the model on a single task or dataset, a continual learning approach involves periodically revisiting previous tasks or safety protocols to prevent catastrophic forgetting. By interleaving safety-related tasks with new training data, the model can retain its safety alignment while adapting to new information.
Additionally, fine-tuning processes can be augmented with adversarial training or robust optimization techniques. By exposing the model to adversarial examples or challenging inputs during fine-tuning, it can learn to resist malicious attacks and maintain its safety alignment in the face of potential vulnerabilities. This adversarial fine-tuning can help improve the robustness of the model against jailbreaking attempts and other security threats.

What other techniques, besides guardrails, can be employed to mitigate the vulnerabilities introduced by fine-tuning and quantization?

In addition to guardrails, several other techniques can be employed to mitigate the vulnerabilities introduced by fine-tuning and quantization in Large Language Models (LLMs). One approach is to implement model monitoring and anomaly detection systems that can flag unusual or potentially harmful behavior in the model's outputs. By continuously monitoring the model's responses and comparing them to expected norms, developers can quickly identify and address any deviations that may indicate security vulnerabilities.
Another technique is to incorporate ensemble methods or model diversity in LLM deployment. By using multiple diverse models or versions of the same model, developers can reduce the impact of vulnerabilities introduced by fine-tuning or quantization. Ensemble methods combine the predictions of multiple models to make more robust and reliable decisions, mitigating the risks associated with individual model vulnerabilities.
Furthermore, secure federated learning techniques can be utilized to train LLMs on distributed data sources without compromising data privacy or security. By leveraging federated learning frameworks, models can be trained collaboratively across multiple devices or servers while ensuring that sensitive information remains protected. This approach can help mitigate the risks of privacy leakage attacks that may arise from fine-tuning or quantization processes.

How can the insights from this research be applied to ensure the responsible development and deployment of large language models in real-world applications?

The insights from this research can be applied to ensure the responsible development and deployment of Large Language Models (LLMs) in real-world applications by incorporating safety considerations at every stage of the model lifecycle. Firstly, developers should prioritize safety and security during the design, training, and fine-tuning phases of LLM development. By integrating safety protocols, alignment training, and guardrails from the outset, models can be better equipped to resist adversarial attacks and maintain ethical standards.
Secondly, continuous monitoring and evaluation of LLMs in production environments are essential to detect and address vulnerabilities promptly. By implementing robust testing frameworks, anomaly detection systems, and model validation procedures, developers can proactively identify and mitigate potential risks before they escalate.
Moreover, collaboration with domain experts, ethicists, and stakeholders is crucial to ensure that LLMs are deployed responsibly and ethically. By engaging in interdisciplinary discussions and incorporating diverse perspectives, developers can address societal concerns, ethical dilemmas, and potential biases that may arise from LLM deployment.
Overall, by applying the insights from this research to real-world applications, developers can promote the safe, reliable, and ethical use of LLMs, fostering trust and transparency in AI technologies.