title: Coercing LLMs to do and reveal (almost) anything
summary: It has recently been shown that adversarial attacks on large language models
  (LLMs) can "jailbreak" the model into making harmful statements. In this work,
  we argue that the spectrum of adversarial attacks on LLMs is much larger than
  merely jailbreaking. We provide a broad overview of possible attack surfaces
  and attack goals. Based on a series of concrete examples, we discuss,
  categorize and systematize attacks that coerce varied unintended behaviors,
  such as misdirection, model control, denial-of-service, or data extraction.
    We analyze these attacks in controlled experiments, and find that many of
  them stem from the practice of pre-training LLMs with coding capabilities, as
  well as the continued existence of strange "glitch" tokens in common LLM
  vocabularies that should be removed for security reasons.
paper_authors._id: 65d7579d81439a41f5ceeb93
paper_authors.name: Jonas Geiping
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-02-22T22:27:02.861Z
paper_authors._id: 63d86dbf3130cadcaf8bdd11
paper_authors.avatarUrl: /avatars/29d79a0c6dcec01111ef192fecd0fa7a.svg
paper_authors.fullname: Jonas Geiping
paper_authors.type: user
paper_authors.user: JonasGeiping
paper_authors._id: 65d7579d81439a41f5ceeb94
paper_authors.name: Alex Stein
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-05-28T15:21:32.230Z
paper_authors._id: 63b88eaaaaa8cf17f097c205
paper_authors.avatarUrl: /avatars/c9f6f3c3ac86def2650d1ecc473cd486.svg
paper_authors.fullname: Alex Stein
paper_authors.type: user
paper_authors.user: astein0
paper_authors._id: 65d7579d81439a41f5ceeb95
paper_authors.name: Manli Shu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-06-19T20:22:17.021Z
paper_authors._id: 63bdcb129a15a3e94198e7d3
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63bdcb129a15a3e94198e7d3/wFVtkekqyQLbS0-78fqcs.jpeg
paper_authors.fullname: Manli Shu
paper_authors.type: user
paper_authors.user: Manli
paper_authors._id: 65d7579d81439a41f5ceeb96
paper_authors.name: Khalid Saifullah
paper_authors._id: 65d7579d81439a41f5ceeb97
paper_authors.name: Yuxin Wen
paper_authors._id: 65d7579d81439a41f5ceeb98
paper_authors.name: Tom Goldstein
paper_title: Coercing LLMs to do and reveal (almost) anything
paper_summary: It has recently been shown that adversarial attacks on large language models
  (LLMs) can "jailbreak" the model into making harmful statements. In this work,
  we argue that the spectrum of adversarial attacks on LLMs is much larger than
  merely jailbreaking. We provide a broad overview of possible attack surfaces
  and attack goals. Based on a series of concrete examples, we discuss,
  categorize and systematize attacks that coerce varied unintended behaviors,
  such as misdirection, model control, denial-of-service, or data extraction.
    We analyze these attacks in controlled experiments, and find that many of
  them stem from the practice of pre-training LLMs with coding capabilities, as
  well as the continued existence of strange "glitch" tokens in common LLM
  vocabularies that should be removed for security reasons.
paper_ai_summary: Adversarial attacks on large language models can cause a variety of unintended behaviors, including misdirection, control, denial-of-service, and data extraction, often due to coding capabilities and unsecured tokens in pre-training.
paper_ai_keywords: adversarial attacks large language models jailbreak unintended behaviors misdirection model control denial-of-service data extraction pre-training coding capabilities glitch tokens