title: Speak Easy: Eliciting Harmful Jailbreaks from LLMs with Simple
    Interactions
summary: Despite extensive safety alignment efforts, large language models (LLMs)
  remain vulnerable to jailbreak attacks that elicit harmful behavior. While
  existing studies predominantly focus on attack methods that require technical
  expertise, two critical questions remain underexplored: (1) Are jailbroken
  responses truly useful in enabling average users to carry out harmful actions?
  (2) Do safety vulnerabilities exist in more common, simple human-LLM
  interactions? In this paper, we demonstrate that LLM responses most effectively
  facilitate harmful actions when they are both actionable and informative--two
  attributes easily elicited in multi-step, multilingual interactions. Using this
  insight, we propose HarmScore, a jailbreak metric that measures how effectively
  an LLM response enables harmful actions, and Speak Easy, a simple multi-step,
  multilingual attack framework. Notably, by incorporating Speak Easy into direct
  request and jailbreak baselines, we see an average absolute increase of 0.319
  in Attack Success Rate and 0.426 in HarmScore in both open-source and
  proprietary LLMs across four safety benchmarks. Our work reveals a critical yet
  often overlooked vulnerability: Malicious users can easily exploit common
  interaction patterns for harmful intentions.
paper_authors._id: 67a5a9357415f9155e9b4b58
paper_authors.name: Yik Siu Chan
paper_authors._id: 67a5a9357415f9155e9b4b59
paper_authors.name: Narutatsu Ri
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-07T09:57:58.519Z
paper_authors._id: 64698ed0dcbb937d56b9dd02
paper_authors.avatarUrl: /avatars/835ce9bf6e2cd1d4b7a709cf41a884e2.svg
paper_authors.fullname: Edward Ri
paper_authors.type: user
paper_authors.user: narutatsuri
paper_authors._id: 67a5a9357415f9155e9b4b5a
paper_authors.name: Yuxin Xiao
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-07T09:58:00.910Z
paper_authors._id: 64bf072bae436c8813494ba3
paper_authors.avatarUrl: /avatars/afb96d2bbf90411f4b1a030ebebff300.svg
paper_authors.fullname: Yuxin Xiao
paper_authors.type: user
paper_authors.user: YuxinXiao
paper_authors._id: 67a5a9357415f9155e9b4b5b
paper_authors.name: Marzyeh Ghassemi
paper_title: Speak Easy: Eliciting Harmful Jailbreaks from LLMs with Simple
    Interactions
paper_summary: Despite extensive safety alignment efforts, large language models (LLMs)
  remain vulnerable to jailbreak attacks that elicit harmful behavior. While
  existing studies predominantly focus on attack methods that require technical
  expertise, two critical questions remain underexplored: (1) Are jailbroken
  responses truly useful in enabling average users to carry out harmful actions?
  (2) Do safety vulnerabilities exist in more common, simple human-LLM
  interactions? In this paper, we demonstrate that LLM responses most effectively
  facilitate harmful actions when they are both actionable and informative--two
  attributes easily elicited in multi-step, multilingual interactions. Using this
  insight, we propose HarmScore, a jailbreak metric that measures how effectively
  an LLM response enables harmful actions, and Speak Easy, a simple multi-step,
  multilingual attack framework. Notably, by incorporating Speak Easy into direct
  request and jailbreak baselines, we see an average absolute increase of 0.319
  in Attack Success Rate and 0.426 in HarmScore in both open-source and
  proprietary LLMs across four safety benchmarks. Our work reveals a critical yet
  often overlooked vulnerability: Malicious users can easily exploit common
  interaction patterns for harmful intentions.
paper_ai_keywords: jailbreak attacks large language models (LLMs) harmful behavior actionable informative multi-step interactions multilingual interactions HarmScore Speak Easy Attack Success Rate