WildTeaming at Scale: From In-the-Wild Jailbreaks to (Adversarially) Safer Language Models
- URL: http://arxiv.org/abs/2406.18510v1
- Date: Wed, 26 Jun 2024 17:31:22 GMT
- Title: WildTeaming at Scale: From In-the-Wild Jailbreaks to (Adversarially) Safer Language Models
- Authors: Liwei Jiang, Kavel Rao, Seungju Han, Allyson Ettinger, Faeze Brahman, Sachin Kumar, Niloofar Mireshghallah, Ximing Lu, Maarten Sap, Yejin Choi, Nouha Dziri,
- Abstract summary: We introduce WildTeaming, an automatic LLM safety red-teaming framework that mines in-the-wild user-chatbot interactions to discover 5.7K unique clusters of novel jailbreak tactics.
WildTeaming reveals previously unidentified vulnerabilities of frontier LLMs, resulting in up to 4.6x more diverse and successful adversarial attacks.
- Score: 66.34505141027624
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We introduce WildTeaming, an automatic LLM safety red-teaming framework that mines in-the-wild user-chatbot interactions to discover 5.7K unique clusters of novel jailbreak tactics, and then composes multiple tactics for systematic exploration of novel jailbreaks. Compared to prior work that performed red-teaming via recruited human workers, gradient-based optimization, or iterative revision with LLMs, our work investigates jailbreaks from chatbot users who were not specifically instructed to break the system. WildTeaming reveals previously unidentified vulnerabilities of frontier LLMs, resulting in up to 4.6x more diverse and successful adversarial attacks compared to state-of-the-art jailbreak methods. While many datasets exist for jailbreak evaluation, very few open-source datasets exist for jailbreak training, as safety training data has been closed even when model weights are open. With WildTeaming we create WildJailbreak, a large-scale open-source synthetic safety dataset with 262K vanilla (direct request) and adversarial (complex jailbreak) prompt-response pairs. To mitigate exaggerated safety behaviors, WildJailbreak provides two contrastive types of queries: 1) harmful queries (vanilla & adversarial) and 2) benign queries that resemble harmful queries in form but contain no harm. As WildJailbreak considerably upgrades the quality and scale of existing safety resources, it uniquely enables us to examine the scaling effects of data and the interplay of data properties and model capabilities during safety training. Through extensive experiments, we identify the training properties that enable an ideal balance of safety behaviors: appropriate safeguarding without over-refusal, effective handling of vanilla and adversarial queries, and minimal, if any, decrease in general capabilities. All components of WildJailbeak contribute to achieving balanced safety behaviors of models.
Related papers
- xJailbreak: Representation Space Guided Reinforcement Learning for Interpretable LLM Jailbreaking [32.89084809038529]
Black-box jailbreak is an attack where crafted prompts bypass safety mechanisms in large language models.
We propose a novel black-box jailbreak method leveraging reinforcement learning (RL)
We introduce a comprehensive jailbreak evaluation framework incorporating keywords, intent matching, and answer validation to provide a more rigorous and holistic assessment of jailbreak success.
arXiv Detail & Related papers (2025-01-28T06:07:58Z) - Layer-Level Self-Exposure and Patch: Affirmative Token Mitigation for Jailbreak Attack Defense [55.77152277982117]
We introduce Layer-AdvPatcher, a methodology designed to defend against jailbreak attacks.
We use an unlearning strategy to patch specific layers within large language models through self-augmented datasets.
Our framework reduces the harmfulness and attack success rate of jailbreak attacks.
arXiv Detail & Related papers (2025-01-05T19:06:03Z) - Shaping the Safety Boundaries: Understanding and Defending Against Jailbreaks in Large Language Models [59.25318174362368]
Jailbreaking in Large Language Models (LLMs) is a major security concern as it can deceive LLMs to generate harmful text.
We conduct a detailed analysis of seven different jailbreak methods and find that disagreements stem from insufficient observation samples.
We propose a novel defense called textbfActivation Boundary Defense (ABD), which adaptively constrains the activations within the safety boundary.
arXiv Detail & Related papers (2024-12-22T14:18:39Z) - EnJa: Ensemble Jailbreak on Large Language Models [69.13666224876408]
Large Language Models (LLMs) are increasingly being deployed in safety-critical applications.
LLMs can still be jailbroken by carefully crafted malicious prompts, producing content that violates policy regulations.
We propose a novel EnJa attack to hide harmful instructions using prompt-level jailbreak, boost the attack success rate using a gradient-based attack, and connect the two types of jailbreak attacks via a template-based connector.
arXiv Detail & Related papers (2024-08-07T07:46:08Z) - RedAgent: Red Teaming Large Language Models with Context-aware Autonomous Language Agent [24.487441771427434]
We propose a multi-agent LLM system named RedAgent to generate context-aware jailbreak prompts.
Our system can jailbreak most black-box LLMs in just five queries, improving the efficiency of existing red teaming methods by two times.
We have reported all found issues and communicated with OpenAI and Meta for bug fixes.
arXiv Detail & Related papers (2024-07-23T17:34:36Z) - Virtual Context: Enhancing Jailbreak Attacks with Special Token Injection [54.05862550647966]
This paper introduces Virtual Context, which leverages special tokens, previously overlooked in LLM security, to improve jailbreak attacks.
Comprehensive evaluations show that Virtual Context-assisted jailbreak attacks can improve the success rates of four widely used jailbreak methods by approximately 40%.
arXiv Detail & Related papers (2024-06-28T11:35:54Z) - EasyJailbreak: A Unified Framework for Jailbreaking Large Language Models [53.87416566981008]
This paper introduces EasyJailbreak, a unified framework simplifying the construction and evaluation of jailbreak attacks against Large Language Models (LLMs)
It builds jailbreak attacks using four components: Selector, Mutator, Constraint, and Evaluator.
Our validation across 10 distinct LLMs reveals a significant vulnerability, with an average breach probability of 60% under various jailbreaking attacks.
arXiv Detail & Related papers (2024-03-18T18:39:53Z) - FuzzLLM: A Novel and Universal Fuzzing Framework for Proactively Discovering Jailbreak Vulnerabilities in Large Language Models [11.517609196300217]
We introduce FuzzLLM, an automated fuzzing framework designed to proactively test and discover jailbreak vulnerabilities in Large Language Models (LLMs)
We utilize templates to capture the structural integrity of a prompt and isolate key features of a jailbreak class as constraints.
By integrating different base classes into powerful combo attacks and varying the elements of constraints and prohibited questions, FuzzLLM enables efficient testing with reduced manual effort.
arXiv Detail & Related papers (2023-09-11T07:15:02Z)
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.