# Dark LLMs: The Growing Threat of Unaligned AI Models

arXiv: https://arxiv.org/abs/2505.10066 · HTML: https://mlsanity.com/paper/2505.10066/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Jailbreaking, one of 12 directions in LLM safety and security.
- Builds on 10 of its 20 references that are in the map.
- First posted on arXiv 15 May 2025.

## Placed in

- LLM safety and security / [Jailbreaking](https://mlsanity.com/safety/jailbreaking/)

## Builds on (most cited first, 10 of 10)

- [Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks](https://mlsanity.com/paper/2404.02151/) (2024)
- [Rethinking Machine Unlearning for Large Language Models](https://mlsanity.com/paper/2402.08787/) (2024)
- [Jailbreak Attacks and Defenses Against Large Language Models: A Survey](https://mlsanity.com/paper/2407.04295/) (2024)
- [WildTeaming at Scale: From In-the-Wild Jailbreaks to (Adversarially) Safer Language Models](https://mlsanity.com/paper/2406.18510/) (2024)
- [LlamaFirewall: An open source guardrail system for building secure AI agents](https://mlsanity.com/paper/2505.03574/) (2025)
- [What Features in Prompts Jailbreak LLMs? Investigating the Mechanisms Behind Attacks](https://mlsanity.com/paper/2411.03343/) (2024)
- [Granite Guardian](https://mlsanity.com/paper/2412.07724/) (2024)
- [Hire a Linguist!: Learning Endangered Languages with In-Context Linguistic Descriptions](https://mlsanity.com/paper/2402.18025/) (2024)
- [Jailbreaking to Jailbreak](https://mlsanity.com/paper/2502.09638/) (2025)
- [On the Origin of LLMs: An Evolutionary Tree and Graph for 15,821 Large Language Models](https://mlsanity.com/paper/2307.09793/) (2023)

## Abstract (arXiv)

Large Language Models (LLMs) rapidly reshape modern life, advancing fields from healthcare to education and beyond. However, alongside their remarkable capabilities lies a significant threat: the susceptibility of these models to jailbreaking. The fundamental vulnerability of LLMs to jailbreak attacks stems from the very data they learn from. As long as this training data includes unfiltered, problematic, or 'dark' content, the models can inherently learn undesirable patterns or weaknesses that allow users to circumvent their intended safety controls. Our research identifies the growing threat posed by dark LLMs models deliberately designed without ethical guardrails or modified through jailbreak techniques. In our research, we uncovered a universal jailbreak attack that effectively compromises multiple state-of-the-art models, enabling them to answer almost any question and produce harmful outputs upon request. The main idea of our attack was published online over seven months ago. However, many of the tested LLMs were still vulnerable to this attack. Despite our responsible disclosure efforts, responses from major LLM providers were often inadequate, highlighting a concerning gap in industry practices regarding AI safety. As model training becomes more accessible and cheaper, and as open-source LLMs proliferate, the risk of widespread misuse escalates. Without decisive intervention, LLMs may continue democratizing access to dangerous knowledge, posing greater risks than anticipated.