Link: http://arxiv.org/abs/2505.10066v1
PDF Link: http://arxiv.org/pdf/2505.10066v1
Summary: Large Language Models (LLMs) rapidly reshape modern life, advancing fieldsfrom healthcare to education and beyond.
However, alongside their remarkablecapabilities lies a significant threat: the susceptibility of these models tojailbreaking.
The fundamental vulnerability of LLMs to jailbreak attacks stemsfrom the very data they learn from.
As long as this training data includesunfiltered, problematic, or 'dark' content, the models can inherently learnundesirable patterns or weaknesses that allow users to circumvent theirintended safety controls.
Our research identifies the growing threat posed bydark LLMs models deliberately designed without ethical guardrails or modifiedthrough jailbreak techniques.
In our research, we uncovered a universaljailbreak attack that effectively compromises multiple state-of-the-art models,enabling them to answer almost any question and produce harmful outputs uponrequest.
The main idea of our attack was published online over seven monthsago.
However, many of the tested LLMs were still vulnerable to this attack.
Despite our responsible disclosure efforts, responses from major LLM providerswere often inadequate, highlighting a concerning gap in industry practicesregarding AI safety.
As model training becomes more accessible and cheaper, andas open-source LLMs proliferate, the risk of widespread misuse escalates.
Without decisive intervention, LLMs may continue democratizing access todangerous knowledge, posing greater risks than anticipated.
Published on arXiv on: 2025-05-15T08:07:04Z