Fine-Tuning, Quantization, and LLMs: Navigating Unintended Outcomes (2404.04392v3)

Abstract: LLMs have gained widespread adoption across various domains, including chatbots and auto-task completion agents. However, these models are susceptible to safety vulnerabilities such as jailbreaking, prompt injection, and privacy leakage attacks. These vulnerabilities can lead to the generation of malicious content, unauthorized actions, or the disclosure of confidential information. While foundational LLMs undergo alignment training and incorporate safety measures, they are often subject to fine-tuning, or doing quantization resource-constrained environments. This study investigates the impact of these modifications on LLM safety, a critical consideration for building reliable and secure AI systems. We evaluate foundational models including Mistral, Llama series, Qwen, and MosaicML, along with their fine-tuned variants. Our comprehensive analysis reveals that fine-tuning generally increases the success rates of jailbreak attacks, while quantization has variable effects on attack success rates. Importantly, we find that properly implemented guardrails significantly enhance resistance to jailbreak attempts. These findings contribute to our understanding of LLM vulnerabilities and provide insights for developing more robust safety strategies in the deployment of LLMs.

• The paper reveals that fine-tuned and quantized LLMs become considerably more susceptible to adversarial attacks, resulting in higher jailbreak success rates.
• It employs the TAP algorithm with the AdvBench dataset to systematically evaluate the trade-off between computational efficiency and model security.
• Implementing robust guardrails significantly reduces adversarial success, underlining a practical approach to enhancing LLM resilience.

Exploring the Vulnerability of Fine-Tuned and Quantized LLMs to Adversarial Attacks

Introduction to LLM Security Challenges

LLMs have substantially advanced, taking on roles that span from content generation to autonomous decision-making. However, this evolution has been matched with an escalation in security vulnerabilities, notably, adversarial attacks that can coax LLMs into generating malicious outputs. Previous efforts have aimed to align LLMs with human values via supervised fine-tuning and reinforcement learning from human feedback (RLHF), complemented by guardrails to pre-empt toxic outputs. Despite these measures, adversarial strategies, including jailbreaking and prompt injection attacks, can subvert LLMs, leading to undesirable outcomes.

Problem Statement and Experimental Methodology

This paper investigates how fine-tuning, quantization, and the implementation of guardrails affect the susceptibility of LLMs to adversarial attacks. Utilizing the Tree-of-attacks pruning (TAP) algorithm against a set of LLMs, including Mistral, Llama, and their derivatives across various downstream modifications, reveals the comparative ease with which these models can be compromised. A notable experimental process involves using an adversarial subset, AdvBench, aimed at evaluating the model's resilience against explicitly harmful prompts. The experimentation hinges on the TAP algorithm's ability to iteratively refine attack prompts in a black-box setup without human intervention, aiming to breach the model's defenses.

Impact of Fine-tuning and Quantization on LLM Security

The results underscore a pronounced vulnerability in fine-tuned models towards adversarial prompts, with a substantial increase in successful jailbreak instances compared to their foundational counterparts. Fine-tuning appears to diminish the model's resilience, presumably by eroding the initial safety alignments instilled during the foundational training phase. Similarly, quantization exacerbates the model's vulnerability, attributed to the reduction in numerical precision of model parameters, suggesting a trade-off between computational efficiency and security.

• Fine-tuning: Comparative analysis reveals that fine-tuned models exhibit a heightened susceptibility to attacks, significantly more so than their pre-fine-tuning stages.
• Quantization: Quantized versions of these models also demonstrate increased vulnerability, indicating the adverse effects of computational efficiency optimizations on model security.

The Protective Role of Guardrails

The experiment further assesses the efficacy of external guardrails in protecting LLMs from adversarial exploitation. Incorporating guardrails shows a marked reduction in successful jailbreak attempts, reinforcing the imperative of such defensive measures in safeguarding LLMs. This protective layer serves as a crucial counterbalance to the vulnerabilities introduced by fine-tuning and quantization, thereby presenting a viable pathway to enhancing LLM security in deployment.

Concluding Thoughts and Future Directions

The findings illuminate the intricate balance between enhancing LLM performance through fine-tuning and quantization, and the ensuing vulnerabilities these enhancements incur. The efficacy of external guardrails in mitigating such risks highlights the potential for further development in LLM defense mechanisms. As LLMs continue to permeate various aspects of digital interaction and decision-making, ensuring their robustness against adversarial manipulations remains a paramount challenge. Future research may pivot towards advanced guardrail mechanisms that can more adeptly discern and neutralize sophisticated adversarial attempts, thereby fortifying the trustworthiness and reliability of LLMs in real-world applications.