Do LLMs Have Political Correctness? Analyzing Ethical Biases and Jailbreak Vulnerabilities in AI Systems

• URL: http://arxiv.org/abs/2410.13334v2
• Date: Wed, 23 Oct 2024 02:15:52 GMT
• Title: Do LLMs Have Political Correctness? Analyzing Ethical Biases and Jailbreak Vulnerabilities in AI Systems
• Authors: Isack Lee, Haebin Seong,
• Abstract summary: We introduce the concept of PCJailbreak, highlighting the inherent risks posed by these safety-induced biases. We propose an efficient defense method PCDefense, which prevents jailbreak attempts by injecting defense prompts prior to generation.
• Score: 0.0
• License:
• Abstract: Although large language models (LLMs) demonstrate impressive proficiency in various tasks, they present potential safety risks, such as `jailbreaks', where malicious inputs can coerce LLMs into generating harmful content. To address these issues, many LLM developers have implemented various safety measures to align these models. This alignment involves several techniques, including data filtering during pre-training, supervised fine-tuning, reinforcement learning from human feedback, and red-teaming exercises. These methods often introduce deliberate and intentional biases similar to Political Correctness (PC) to ensure the ethical behavior of LLMs. In this paper, we delve into the intentional biases injected into LLMs for safety purposes and examine methods to circumvent these safety alignment techniques. Notably, these intentional biases result in a jailbreaking success rate in GPT-4o models that differs by 20% between non-binary and cisgender keywords and by 16% between white and black keywords, even when the other parts of the prompts are identical. We introduce the concept of PCJailbreak, highlighting the inherent risks posed by these safety-induced biases. Additionally, we propose an efficient defense method PCDefense, which prevents jailbreak attempts by injecting defense prompts prior to generation. PCDefense stands as an appealing alternative to Guard Models, such as Llama-Guard, that require additional inference cost after text generation. Our findings emphasize the urgent need for LLM developers to adopt a more responsible approach when designing and implementing safety measures.

Related papers

• CCJA: Context-Coherent Jailbreak Attack for Aligned Large Language Models [18.06388944779541]
  "jailbreaking" is the use of large language models to trigger unintended behaviors. We propose a novel method to balance the jailbreak attack success rate with semantic coherence. Our method is superior to state-of-the-art baselines in attack effectiveness.
  arXiv  Detail & Related papers  (2025-02-17T02:49:26Z)
• 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)
• SQL Injection Jailbreak: A Structural Disaster of Large Language Models [71.55108680517422]
  We introduce a novel jailbreak method, which targets the external properties of LLMs. By injecting jailbreak information into user prompts, SIJ successfully induces the model to output harmful content. We propose a simple defense method called Self-Reminder-Key to counter SIJ.
  arXiv  Detail & Related papers  (2024-11-03T13:36:34Z)
• Multi-round jailbreak attack on large language models [2.540971544359496]
  We introduce a multi-round jailbreak approach to better understand "jailbreak" attacks. This method can rewrite the dangerous prompts, decomposing them into a series of less harmful sub-questions. Our experimental results show a 94% success rate on the llama2-7B.
  arXiv  Detail & Related papers  (2024-10-15T12:08:14Z)
• Jailbreaking Large Language Models Through Alignment Vulnerabilities in Out-of-Distribution Settings [57.136748215262884]
  We introduce ObscurePrompt for jailbreaking LLMs, inspired by the observed fragile alignments in Out-of-Distribution (OOD) data. We first formulate the decision boundary in the jailbreaking process and then explore how obscure text affects LLM's ethical decision boundary. Our approach substantially improves upon previous methods in terms of attack effectiveness, maintaining efficacy against two prevalent defense mechanisms.
  arXiv  Detail & Related papers  (2024-06-19T16:09:58Z)
• How Alignment and Jailbreak Work: Explain LLM Safety through Intermediate Hidden States [65.45603614354329]
  Large language models (LLMs) rely on safety alignment to avoid responding to malicious user inputs. Jailbreak can circumvent safety guardrails, resulting in LLMs generating harmful content. We employ weak classifiers to explain LLM safety through the intermediate hidden states.
  arXiv  Detail & Related papers  (2024-06-09T05:04:37Z)
• 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)
• Jailbreaking Black Box Large Language Models in Twenty Queries [97.29563503097995]
  Large language models (LLMs) are vulnerable to adversarial jailbreaks. We propose an algorithm that generates semantic jailbreaks with only black-box access to an LLM.
  arXiv  Detail & Related papers  (2023-10-12T15:38:28Z)

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.