Coercing LLMs to do and reveal (almost) anything
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.
65d7579d81439a41f5ceeb93
Jonas Geiping
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2024-02-22T22:27:02.861Z
63d86dbf3130cadcaf8bdd11
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Jonas Geiping
user
JonasGeiping
65d7579d81439a41f5ceeb94
Alex Stein
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2024-05-28T15:21:32.230Z
63b88eaaaaa8cf17f097c205
/avatars/c9f6f3c3ac86def2650d1ecc473cd486.svg
Alex Stein
user
astein0
65d7579d81439a41f5ceeb95
Manli Shu
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2024-06-19T20:22:17.021Z
63bdcb129a15a3e94198e7d3
https://cdn-avatars.huggingface.co/v1/production/uploads/63bdcb129a15a3e94198e7d3/wFVtkekqyQLbS0-78fqcs.jpeg
Manli Shu
user
Manli
65d7579d81439a41f5ceeb96
Khalid Saifullah
65d7579d81439a41f5ceeb97
Yuxin Wen
65d7579d81439a41f5ceeb98
Tom Goldstein
Coercing LLMs to do and reveal (almost) anything
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.
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.
adversarial attacks large language models jailbreak unintended behaviors misdirection model control denial-of-service data extraction pre-training coding capabilities glitch tokens