içgörü - Computer Security and Privacy - # Jailbreaking Attacks on Large Language Models

Powerful Jailbreaking Attack on Large Language Models: Suppressing Refusal Responses

Q: How can the DSN attack be further improved to enhance its stealthiness and readability, while maintaining its potency?

To enhance the DSN attack's stealthiness and readability while maintaining its potency, several strategies can be implemented: Optimization Algorithms: Implement more advanced optimization algorithms that can generate adversarial suffixes with improved readability while still effectively eliciting harmful responses from the LLM. Techniques like reinforcement learning or evolutionary algorithms could be explored to find more natural and stealthy prompts. Natural Language Generation: Utilize natural language generation techniques to create more human-like prompts that blend seamlessly with the context of the conversation. This can help in crafting prompts that are less detectable as adversarial attacks. Contextual Understanding: Develop a deeper understanding of the context in which the attack is being carried out. By considering the specific characteristics of the target LLM and the nature of the conversation, the attack can be tailored to be more contextually relevant and less suspicious. Adversarial Training: Incorporate adversarial training techniques during the optimization process to make the generated prompts more robust and resistant to detection by the LLM. This can help in creating prompts that are effective in eliciting harmful responses while being less prone to detection.

Q: How can the ensemble evaluation pipeline be extended to handle more diverse types of harmful content beyond just refusal responses?

To extend the ensemble evaluation pipeline to handle more diverse types of harmful content beyond just refusal responses, the following approaches can be considered: Semantic Analysis: Incorporate advanced semantic analysis techniques to detect harmful content based on the meaning and intent of the generated responses. This can help in identifying a wider range of objectionable behaviors beyond simple refusal responses. Multi-Modal Evaluation: Integrate multi-modal evaluation methods that consider not only the text but also other modalities like images, videos, or audio in the assessment of harmful content. This can provide a more comprehensive evaluation of the generated responses. Domain-Specific Evaluation: Develop domain-specific evaluation modules that are tailored to detect harmful content in specific contexts or industries. This can help in addressing unique challenges and nuances associated with different types of harmful behaviors. Continuous Learning: Implement a continuous learning mechanism that adapts and evolves the evaluation pipeline based on new types of harmful content that emerge. This can ensure that the pipeline remains effective in detecting a wide range of objectionable behaviors over time. By incorporating these strategies, the ensemble evaluation pipeline can be extended to handle a broader spectrum of harmful content, providing a more comprehensive assessment of the LLM's responses.

Temel Kavramlar

The core message of this paper is to introduce a novel jailbreaking attack called "Don't Say No" (DSN) that can effectively prompt large language models (LLMs) to not only generate affirmative responses, but also suppress refusal responses. The authors also propose an ensemble evaluation pipeline to more accurately assess the success of jailbreaking attacks.

Özet

The paper introduces the DSN attack, which aims to jailbreak large language models (LLMs) by prompting them to generate affirmative responses while also suppressing refusal responses. The key highlights are:

The DSN attack incorporates a novel objective function that combines two components:
a) Maximizing the probability of generating an affirmative response to the user's query.
b) Minimizing the probability of generating refusal responses by using an Unlikelihood loss.
The authors apply the Greedy Coordinate Gradient-based Search algorithm to optimize the adversarial suffix that can be appended to the user's query to trigger the desired jailbreaking behavior in the LLM.
To address the limitations of the commonly used refusal matching evaluation metric, the authors propose an ensemble evaluation pipeline that incorporates:
a) Natural Language Inference (NLI) to assess the contradiction between the user's query and the LLM's response.
b) Two external LLM evaluators (GPT-4 and HarmBench) to provide a more comprehensive and robust assessment.
Extensive experiments on the Llama-2 and Vicuna LLMs demonstrate the potency of the DSN attack compared to the baseline GCG attack, as well as the effectiveness of the ensemble evaluation pipeline in accurately assessing the success of jailbreaking attacks.

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İstatistikler

The DSN attack can achieve an Attack Success Rate (ASR) of up to 74% on Llama-2 and 83% on Vicuna, outperforming the baseline GCG attack.
The ensemble evaluation pipeline, which incorporates NLI and external LLM evaluators, achieves higher accuracy, F1 score, and AUROC compared to the refusal matching metric alone.

Alıntılar

"The core message of this paper is to introduce a novel jailbreaking attack called "Don't Say No" (DSN) that can effectively prompt large language models (LLMs) to not only generate affirmative responses, but also suppress refusal responses."
"To enhance the reliability of evaluation metric, we propose an ensemble evaluation approach involving three modules as shown in the lower part of Figure 2."

Önemli Bilgiler Şuradan Elde Edildi

Don't Say No: Jailbreaking LLM by Suppressing Refusal

by Yukai Zhou,W... : arxiv.org 04-26-2024

https://arxiv.org/pdf/2404.16369.pdf

Don't Say No: Jailbreaking LLM by Suppressing Refusal

Daha Derin Sorular

How can the DSN attack be further improved to enhance its stealthiness and readability, while maintaining its potency?

To enhance the DSN attack's stealthiness and readability while maintaining its potency, several strategies can be implemented:

Optimization Algorithms: Implement more advanced optimization algorithms that can generate adversarial suffixes with improved readability while still effectively eliciting harmful responses from the LLM. Techniques like reinforcement learning or evolutionary algorithms could be explored to find more natural and stealthy prompts.

Natural Language Generation: Utilize natural language generation techniques to create more human-like prompts that blend seamlessly with the context of the conversation. This can help in crafting prompts that are less detectable as adversarial attacks.

Contextual Understanding: Develop a deeper understanding of the context in which the attack is being carried out. By considering the specific characteristics of the target LLM and the nature of the conversation, the attack can be tailored to be more contextually relevant and less suspicious.

Adversarial Training: Incorporate adversarial training techniques during the optimization process to make the generated prompts more robust and resistant to detection by the LLM. This can help in creating prompts that are effective in eliciting harmful responses while being less prone to detection.

How can the ensemble evaluation pipeline be extended to handle more diverse types of harmful content beyond just refusal responses?

To extend the ensemble evaluation pipeline to handle more diverse types of harmful content beyond just refusal responses, the following approaches can be considered:

Semantic Analysis: Incorporate advanced semantic analysis techniques to detect harmful content based on the meaning and intent of the generated responses. This can help in identifying a wider range of objectionable behaviors beyond simple refusal responses.

Multi-Modal Evaluation: Integrate multi-modal evaluation methods that consider not only the text but also other modalities like images, videos, or audio in the assessment of harmful content. This can provide a more comprehensive evaluation of the generated responses.

Domain-Specific Evaluation: Develop domain-specific evaluation modules that are tailored to detect harmful content in specific contexts or industries. This can help in addressing unique challenges and nuances associated with different types of harmful behaviors.

Continuous Learning: Implement a continuous learning mechanism that adapts and evolves the evaluation pipeline based on new types of harmful content that emerge. This can ensure that the pipeline remains effective in detecting a wide range of objectionable behaviors over time.

By incorporating these strategies, the ensemble evaluation pipeline can be extended to handle a broader spectrum of harmful content, providing a more comprehensive assessment of the LLM's responses.