Protecting Your LLMs with Information Bottleneck
- URL: http://arxiv.org/abs/2404.13968v2
- Date: Thu, 16 May 2024 13:26:57 GMT
- Title: Protecting Your LLMs with Information Bottleneck
- Authors: Zichuan Liu, Zefan Wang, Linjie Xu, Jinyu Wang, Lei Song, Tianchun Wang, Chunlin Chen, Wei Cheng, Jiang Bian,
- Abstract summary: We introduce the Information Bottleneck Protector (IBProtector), a defense mechanism grounded in the information bottleneck principle.
The IBProtector selectively compresses and perturbs prompts, facilitated by a lightweight and trainable extractor.
Our empirical evaluations show that IBProtector outperforms current defense methods in mitigating jailbreak attempts.
- Score: 20.870610473199125
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The advent of large language models (LLMs) has revolutionized the field of natural language processing, yet they might be attacked to produce harmful content. Despite efforts to ethically align LLMs, these are often fragile and can be circumvented by jailbreaking attacks through optimized or manual adversarial prompts. To address this, we introduce the Information Bottleneck Protector (IBProtector), a defense mechanism grounded in the information bottleneck principle, and we modify the objective to avoid trivial solutions. The IBProtector selectively compresses and perturbs prompts, facilitated by a lightweight and trainable extractor, preserving only essential information for the target LLMs to respond with the expected answer. Moreover, we further consider a situation where the gradient is not visible to be compatible with any LLM. Our empirical evaluations show that IBProtector outperforms current defense methods in mitigating jailbreak attempts, without overly affecting response quality or inference speed. Its effectiveness and adaptability across various attack methods and target LLMs underscore the potential of IBProtector as a novel, transferable defense that bolsters the security of LLMs without requiring modifications to the underlying models.
Related papers
- Purple-teaming LLMs with Adversarial Defender Training [57.535241000787416]
We present Purple-teaming LLMs with Adversarial Defender training (PAD)
PAD is a pipeline designed to safeguard LLMs by novelly incorporating the red-teaming (attack) and blue-teaming (safety training) techniques.
PAD significantly outperforms existing baselines in both finding effective attacks and establishing a robust safe guardrail.
arXiv Detail & Related papers (2024-07-01T23:25:30Z) - Defensive Prompt Patch: A Robust and Interpretable Defense of LLMs against Jailbreak Attacks [59.46556573924901]
This paper introduces Defensive Prompt Patch (DPP), a novel prompt-based defense mechanism for large language models (LLMs)
Unlike previous approaches, DPP is designed to achieve a minimal Attack Success Rate (ASR) while preserving the high utility of LLMs.
Empirical results conducted on LLAMA-2-7B-Chat and Mistral-7B-Instruct-v0.2 models demonstrate the robustness and adaptability of DPP.
arXiv Detail & Related papers (2024-05-30T14:40:35Z) - Defending Large Language Models Against Jailbreak Attacks via Layer-specific Editing [14.094372002702476]
Large language models (LLMs) are increasingly being adopted in a wide range of real-world applications.
Recent studies have shown that LLMs are vulnerable to deliberately crafted adversarial prompts.
We propose a novel defense method termed textbfLayer-specific textbfEditing (LED) to enhance the resilience of LLMs against jailbreak attacks.
arXiv Detail & Related papers (2024-05-28T13:26:12Z) - Robustifying Safety-Aligned Large Language Models through Clean Data Curation [11.273749179260468]
Large language models (LLMs) are vulnerable when trained on datasets containing harmful content.
In this paper, we propose a data curation framework designed to counter adversarial impacts in both scenarios.
arXiv Detail & Related papers (2024-05-24T04:50:38Z) - AdaShield: Safeguarding Multimodal Large Language Models from Structure-based Attack via Adaptive Shield Prompting [54.931241667414184]
We propose textbfAdaptive textbfShield Prompting, which prepends inputs with defense prompts to defend MLLMs against structure-based jailbreak attacks.
Our methods can consistently improve MLLMs' robustness against structure-based jailbreak attacks.
arXiv Detail & Related papers (2024-03-14T15:57:13Z) - A Wolf in Sheep's Clothing: Generalized Nested Jailbreak Prompts can Fool Large Language Models Easily [51.63085197162279]
Large Language Models (LLMs) are designed to provide useful and safe responses.
adversarial prompts known as 'jailbreaks' can circumvent safeguards.
We propose ReNeLLM, an automatic framework that leverages LLMs themselves to generate effective jailbreak prompts.
arXiv Detail & Related papers (2023-11-14T16:02:16Z) - Attack Prompt Generation for Red Teaming and Defending Large Language
Models [70.157691818224]
Large language models (LLMs) are susceptible to red teaming attacks, which can induce LLMs to generate harmful content.
We propose an integrated approach that combines manual and automatic methods to economically generate high-quality attack prompts.
arXiv Detail & Related papers (2023-10-19T06:15:05Z) - SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks [99.23352758320945]
We propose SmoothLLM, the first algorithm designed to mitigate jailbreaking attacks on large language models (LLMs)
Based on our finding that adversarially-generated prompts are brittle to character-level changes, our defense first randomly perturbs multiple copies of a given input prompt, and then aggregates the corresponding predictions to detect adversarial inputs.
arXiv Detail & Related papers (2023-10-05T17:01:53Z) - Defending Against Alignment-Breaking Attacks via Robustly Aligned LLM [23.16217797677075]
We introduce a Robustly Aligned LLM (RA-LLM) to defend against potential alignment-breaking attacks.
RA-LLM can successfully defend against both state-of-the-art adversarial prompts and popular handcrafted jailbreaking prompts.
arXiv Detail & Related papers (2023-09-18T02:07:22Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.