Understanding Jailbreak Success: A Study of Latent Space Dynamics in Large Language Models
- URL: http://arxiv.org/abs/2406.09289v1
- Date: Thu, 13 Jun 2024 16:26:47 GMT
- Title: Understanding Jailbreak Success: A Study of Latent Space Dynamics in Large Language Models
- Authors: Sarah Ball, Frauke Kreuter, Nina Rimsky,
- Abstract summary: This paper analyses model activations on different jailbreak inputs.
We find that it is possible to extract a jailbreak vector from a single class of jailbreaks that works to mitigate jailbreak effectiveness from other classes.
- Score: 4.547063832007314
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Conversational Large Language Models are trained to refuse to answer harmful questions. However, emergent jailbreaking techniques can still elicit unsafe outputs, presenting an ongoing challenge for model alignment. To better understand how different jailbreak types circumvent safeguards, this paper analyses model activations on different jailbreak inputs. We find that it is possible to extract a jailbreak vector from a single class of jailbreaks that works to mitigate jailbreak effectiveness from other classes. This may indicate that different kinds of effective jailbreaks operate via similar internal mechanisms. We investigate a potential common mechanism of harmfulness feature suppression, and provide evidence for its existence by looking at the harmfulness vector component. These findings offer actionable insights for developing more robust jailbreak countermeasures and lay the groundwork for a deeper, mechanistic understanding of jailbreak dynamics in language models.
Related papers
- 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) - WildTeaming at Scale: From In-the-Wild Jailbreaks to (Adversarially) Safer Language Models [66.34505141027624]
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.
arXiv Detail & Related papers (2024-06-26T17:31:22Z) - Knowledge-to-Jailbreak: One Knowledge Point Worth One Attack [86.6931690001357]
Knowledge-to-jailbreak aims to generate jailbreaks from domain knowledge to evaluate the safety of large language models on specialized domains.
We collect a large-scale dataset with 12,974 knowledge-jailbreak pairs and fine-tune a large language model as jailbreak-generator.
arXiv Detail & Related papers (2024-06-17T15:59:59Z) - JailbreakEval: An Integrated Toolkit for Evaluating Jailbreak Attempts Against Large Language Models [21.854909839996612]
Jailbreak attacks aim to induce Large Language Models (LLMs) to generate harmful responses for forbidden instructions.
There is (surprisingly) no consensus on how to evaluate whether a jailbreak attempt is successful.
JailbreakEval is a user-friendly toolkit focusing on the evaluation of jailbreak attempts.
arXiv Detail & Related papers (2024-06-13T16:59:43Z) - JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models [123.66104233291065]
Jailbreak attacks cause large language models (LLMs) to generate harmful, unethical, or otherwise objectionable content.
evaluating these attacks presents a number of challenges, which the current collection of benchmarks and evaluation techniques do not adequately address.
JailbreakBench is an open-sourced benchmark with the following components.
arXiv Detail & Related papers (2024-03-28T02:44:02Z) - Don't Listen To Me: Understanding and Exploring Jailbreak Prompts of Large Language Models [29.312244478583665]
generative AI has enabled ubiquitous access to large language models (LLMs)
Jailbreak prompts have emerged as one of the most effective mechanisms to circumvent security restrictions and elicit harmful content originally designed to be prohibited.
We show that users often succeeded in jailbreak prompts generation regardless of their expertise in LLMs.
We also develop a system using AI as the assistant to automate the process of jailbreak prompt generation.
arXiv Detail & Related papers (2024-03-26T02:47:42Z) - 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) - A StrongREJECT for Empty Jailbreaks [74.66228107886751]
There is no standard benchmark for measuring the severity of a jailbreak.
We present StrongREJECT, which better discriminates between effective and ineffective jailbreaks.
arXiv Detail & Related papers (2024-02-15T18:58:09Z) - 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) - Jailbreaking ChatGPT via Prompt Engineering: An Empirical Study [22.411634418082368]
Large Language Models (LLMs) have demonstrated vast potential but also introduce challenges related to content constraints and potential misuse.
Our study investigates three key research questions: (1) the number of different prompt types that can jailbreak LLMs, (2) the effectiveness of jailbreak prompts in circumventing LLM constraints, and (3) the resilience of ChatGPT against these jailbreak prompts.
arXiv Detail & Related papers (2023-05-23T09:33:38Z)
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.