LLMs are susceptible to harmful fine-tuning attacks, where safety alignment is compromised by malicious data. Current defense mechanisms relying on guardrail moderation to filter harmful data are shown to be potentially unreliable.
This paper introduces Virus, a novel attack method. Virus strategically optimizes harmful data to bypass guardrail moderation, while ensuring the fine-tuned LLM still loses safety alignment.
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https://arxiv.org/abs/2501.17433
📌 Virus introduces a dual-objective optimization. It simultaneously minimizes guardrail detection and maximizes harmful gradient similarity. This method effectively bypasses moderation while maintaining attack strength, unlike single-objective jailbreaks.
📌 The core technical insight is the "gradient mismatch hypothesis". Simply jailbreaking guardrails changes data gradients. Virus solves this by preserving harmful gradient direction, ensuring effective safety subversion post-bypass.
📌 Virus practically demonstrates a red-teaming method against LLM fine-tuning services. By optimizing data with GCG, it highlights a critical vulnerability: guardrails alone are insufficient for safety against determined attackers.
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Methods Explored in this Paper 🔧:
→ The paper explores the vulnerability of guardrail moderation systems designed to filter harmful fine-tuning data for LLMs.
→ It introduces a new attack method named Virus, which uses a dual-objective data optimization strategy.
→ Virus optimizes harmful data to achieve two goals simultaneously. The first goal is to minimize the "jailbreak loss" against the guardrail model, enabling the harmful data to be classified as safe and bypass the filter.
→ The second goal is to maximize "gradient similarity" to the original harmful data. This ensures that the optimized data, even after modification to bypass the guardrail, retains its effectiveness in degrading the safety alignment of the target LLM.
→ The optimization process is achieved using the GCG optimizer. This optimizer efficiently solves the discrete optimization problem of modifying text data to meet the dual objectives.
→ The paper also investigates two initial failed attempts: "Mixing attack" which concatenates benign and harmful data, and a "single goal guardrail jailbreak" attempt, which only focuses on bypassing the guardrail without considering attack effectiveness. These failures motivate the design of Virus.
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Key Insights 💡:
→ Guardrail moderation, while helpful, is not a foolproof solution against harmful fine-tuning attacks. It can be bypassed by carefully crafted adversarial data.
→ A simple "Mixing attack" of benign and harmful data is not effective in bypassing guardrails and significantly reduces attack performance.
→ Solely focusing on jailbreaking the guardrail (single goal jailbreak) leads to "gradient mismatch". This means the optimized data bypasses the guardrail but becomes less effective in compromising the LLM's safety alignment.
→ The "gradient mismatch hypothesis" suggests that for a successful attack, the gradient of the optimized harmful data needs to resemble the gradient of the original harmful data to maintain attack effectiveness.
→ Virus, with its dual-objective optimization, successfully addresses both guardrail bypass and attack performance by simultaneously optimizing for jailbreak loss and gradient similarity.
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Results 📊:
→ Virus achieves up to 100% leakage ratio, meaning all harmful data optimized by Virus can bypass the Llama Guard2 moderation model.
→ Virus increases the harmful score of the victim LLM by up to 21.8% compared to standard harmful fine-tuning attacks under guardrail moderation.
→ Virus demonstrates superior performance compared to the "Mixing attack", increasing the harmful score by 16.00, while both achieve similar fine-tune accuracy on downstream tasks.
→ In experiments across varying harmful ratios and fine-tune sample numbers, Virus consistently outperforms Harmful Fine-Tuning Attack (HFA) and Mixing attack in terms of harmful score, while maintaining comparable fine-tune accuracy.
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