title: Do LLMs Have Political Correctness? Analyzing Ethical Biases and
    Jailbreak Vulnerabilities in AI Systems
summary: 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.
paper_authors._id: 671210b6d86855660a264b96
paper_authors.name: Isack Lee
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-21T07:48:12.573Z
paper_authors._id: 655c2f00360e4f90cb72cd90
paper_authors.avatarUrl: /avatars/b1c617eeeb24eeae8c4c0c6884534544.svg
paper_authors.fullname: lee isack
paper_authors.type: user
paper_authors.user: toast224
paper_authors._id: 671210b6d86855660a264b97
paper_authors.name: Haebin Seong
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-10-18T07:39:40.919Z
paper_authors._id: 63a9379e2e05ca32e352d93b
paper_authors.avatarUrl: /avatars/6cda37befc873a92ed6d5dcba507954a.svg
paper_authors.fullname: Haebin Seong
paper_authors.type: user
paper_authors.user: hbseong
paper_title: Do LLMs Have Political Correctness? Analyzing Ethical Biases and
    Jailbreak Vulnerabilities in AI Systems
paper_summary: 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.
paper_ai_keywords: large language models (LLMs) jailbreaks malicious inputs harmful content pre-training scene fine-tuning reinforcement learning from human feedback red-teaming exercises Political Correctness (PC) ethical behavior intentional biases jailbreaking success rate PCJailbreak PCDefense Guard Models (e.g., Llama-Guard)