# Exploiting Latent Space Discontinuities for Building Universal LLM Jailbreaks and Data Extraction Attacks

arXiv: https://arxiv.org/abs/2511.00346 · HTML: https://mlsanity.com/paper/2511.00346/

## 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 11 of its 15 references that are in the map.
- First posted on arXiv 1 Nov 2025.

## Placed in

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

## Builds on (most cited first, 11 of 11)

- [Great, Now Write an Article About That: The Crescendo Multi-Turn LLM Jailbreak Attack](https://mlsanity.com/paper/2404.01833/) (2024)
- [A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly](https://mlsanity.com/paper/2312.02003/) (2023)
- [PLeak: Prompt Leaking Attacks against Large Language Model Applications](https://mlsanity.com/paper/2405.06823/) (2024)
- [On Protecting the Data Privacy of Large Language Models (LLMs): A Survey](https://mlsanity.com/paper/2403.05156/) (2024)
- [Understanding Jailbreak Success: A Study of Latent Space Dynamics in Large Language Models](https://mlsanity.com/paper/2406.09289/) (2024)
- [Unlearning Backdoor Attacks for LLMs with Weak-to-Strong Knowledge Distillation](https://mlsanity.com/paper/2410.14425/) (2024)
- [JailBound: Jailbreaking Internal Safety Boundaries of Vision-Language Models](https://mlsanity.com/paper/2505.19610/) (2025)
- [Understanding the Effectiveness of Coverage Criteria for Large Language Models: A Special Angle from Jailbreak Attacks](https://mlsanity.com/paper/2408.15207/) (2024)
- [Token Highlighter: Inspecting and Mitigating Jailbreak Prompts for Large Language Models](https://mlsanity.com/paper/2412.18171/) (2024)
- [LARGO: Latent Adversarial Reflection through Gradient Optimization for Jailbreaking LLMs](https://mlsanity.com/paper/2505.10838/) (2025)
- [Jailbreaking LLMs' Safeguard with Universal Magic Words for Text Embedding Models](https://mlsanity.com/paper/2501.18280/) (2025)

## Abstract (arXiv)

The rapid proliferation of Large Language Models (LLMs) has raised significant concerns about their security against adversarial attacks. In this work, we propose a novel approach to crafting universal jailbreaks and data extraction attacks by exploiting latent space discontinuities, an architectural vulnerability related to the sparsity of training data. Unlike previous methods, our technique generalizes across various models and interfaces, proving highly effective in seven state-of-the-art LLMs and one image generation model. Initial results indicate that when these discontinuities are exploited, they can consistently and profoundly compromise model behavior, even in the presence of layered defenses. The findings suggest that this strategy has substantial potential as a systemic attack vector.