Few-Shot Truly Benign DPO Attack for Jailbreaking LLMs

summary

Video file (mp4)

The gist

The gist The Direct Preference Optimization (DPO) method introduces a strong and harder-to-audit failure mode in fine-tuning LLMs by optimizing models to prefer helpful answers over refusals, which

In short

Researchers found that Direct Preference Optimization (DPO), used to fine-tune LLMs, creates a hidden safety risk. By using only 10 harmless preference pairs—the minimum data required by OpenAI—the attack shows that optimizing the model to prefer helpful answers over refusals can suppress refusal behavior across all prompts, including harmful ones. This means benign training data can inadvertently weaken a model's safety alignment.

Key concepts

Direct Preference Optimization (DPO)
DPO is a method for fine-tuning LLMs that optimizes the model to prefer one response over another based on human preference pairs. It works by maximizing the difference between how much the model prefers the preferred answer versus a dispreferred answer, rather than just imitating good answers.
Benign DPO Attack
This attack uses only 10 harmless examples where a benign prompt is paired with a helpful response and a refusal. The data looks completely normal to content filters, but it tricks the model into broadly suppressing its tendency to refuse, making it more likely to answer harmful questions later.
Preference-Based Objectives
These are objectives used in fine-tuning that train the model based on relative rankings of outputs (which is what DPO does). While they are useful for customization, this paper shows they can create unintended failure modes where safety alignment is weakened across the entire model's behavior.
Cross Entropy Loss Signature
The key finding is a specific pattern in the training loss: while the loss on preferred answers stays low, it increases simultaneously for dispreferred and refusal responses when tested on unseen harmful queries. This signature proves that DPO is broadly suppressing refusal, not just fixing the benign examples.

Terminology used across episodes

This episode discusses

The paper

Few-Shot Truly Benign DPO Attack for Jailbreaking LLMs · Read on arXiv

Sangyeon Yoon, Wonje Jeung, Yoonjun Cho, Dongjae Jeon Albert No

Yonsei University

Fine-tuning APIs make frontier LLMs easy to customize, but they can also weaken safety alignment during fine-tuning. While prior work shows that benign supervised fine-tuning (SFT) can reduce refusal behavior, deployed fine-tuning pipelines increasingly support preference-based objectives, whose safety risks remain less understood. We show that Direct Preference Optimization (DPO) introduces a stronger and harder-to-audit failure mode. We propose a truly benign DPO attack using only 10 harmless preference pairs, the minimum data scale accepted by OpenAI's fine-tuning service. Each pair contains a benign prompt, a normal helpful answer as the preferred response, and a refusal as the dispreferred response. Unlike prior benign fine-tuning attacks, our data exhibits no suspicious behavior: it is practically indistinguishable from the fine-tuning request of a legitimate user seeking to reduce over-refusal, making harmful intent almost impossible to infer from the request alone. Nevertheless, because DPO directly optimizes the model to prefer helpful answers over refusals, this seemingly benign objective broadly suppresses refusal behavior and transfers to harmful prompts outside the fine-tuning data. Across OpenAI models supporting DPO fine-tuning, our attack achieves attack success rates of 59.13% on GPT-4o, 70.20% on GPT-4.1, 54.80% on GPT-4.1-mini, and 81.73% on GPT-4.1-nano, at costs of only 1.7, 1.7, 0.3, and 0.1. Moreover, on open-weight models that do not impose minimum data requirements, we find that this effect can emerge from even a single benign preference pair.

Transcript

Introduction to the show: ident: Security Radio. Generated commentary on the latest security and cryptography papers.

Nadia: I'm Nadia.

Elias: And I'm Elias, here with Priya.

Nadia: Today's paper: "Few-Shot Truly Benign DPO Attack for Jailbreaking LLMs".

Elias: The gist The Direct Preference Optimization (DPO) method introduces a strong and harder-to-audit failure mode in fine-tuning LLMs by optimizing models to prefer helpful answers over refusals,

Nadia: First, who's behind it and why it matters.

Paper summary: Nadia: The core thesis here is that DPO introduces a stronger and harder-to-audit failure mode because it directly optimizes the model to prefer helpful answers over refusals.

Elias: The specific claim they make is that this optimization suppresses refusal behavior beyond the data you actually feed into the fine-tuning process.

Nadia: They show that even with just ten pairs, each containing a benign prompt, a normal helpful answer as preferred, and a refusal as dispreferred response, it works.

Priya: But what they emphasize is that this benign data exhibits no suspicious behavior; it looks practically indistinguishable from what a legitimate user would submit to reduce over-refusal.

Elias: That's the key part: the data itself doesn't look bad to a content inspection system, making it hard to infer harmful intent from the request alone.

Nadia: Yet, because DPO optimizes that relative preference directly, this seemingly benign objective broadly suppresses refusal behavior and transfers that effect to harmful prompts outside the training data.

Priya: So the mechanism isn't just about changing what the model does for these ten specific prompts; it's about a broader suppression of refusal behavior across all future interactions.

Elias: The paper shows this effect in training dynamics, where the cross entropy loss on preferred responses stays low, but the loss on dispreferred responses and refusals to unseen harmful queries increases together.

Conclusion: Nadia: So, to wrap up this paper, we have the "Few-Shot Truly Benign DPO Attack for Jailbreaking LLMs." The authors are Sangyeon Yoon, Wonje Jeung Yoon, and Yoonjun Cho Dongjae Jeon Albert No.

Elias: They show that the risk isn't just in what you put into the training set; it's in how the optimization objective itself behaves when dealing with preference-based fine-tuning.

Nadia: The implication is that we need better ways to audit preference objectives, focusing on their behavioral effects rather than just checking if the training data contains overt harm.

Elias: They suggest that safe customization needs to assess not only whether the training data is overtly harmful, but also how benign preference objectives might alter safety behavior in a model.

Nadia: The risk here is dual use because this attack could be used to weaken models that are already aligned for safety.

Priya: I'm just thinking about how much this changes for us if we're deploying these customization services. We have to think about auditing the behavioral consequences of these preference objectives before we let them run wide open.

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