# Boosting Jailbreak Transferability for Large Language Models

arXiv: https://arxiv.org/abs/2410.15645 · HTML: https://mlsanity.com/paper/2410.15645/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Jailbreaking, one of 12 directions in LLM safety and security.
- Builds on 2 of its 8 references that are in the map.
- First posted on arXiv 21 Oct 2024.

## Placed in

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

## Builds on (most cited first, 2 of 2)

- [Universal and Transferable Adversarial Attacks on Aligned Language Models](https://mlsanity.com/paper/2307.15043/) (2023)
- [Red Teaming GPT-4V: Are GPT-4V Safe Against Uni/Multi-Modal Jailbreak Attacks?](https://mlsanity.com/paper/2404.03411/) (2024)

## Built on by (most cited first, 5 of 5)

- [Large Language Model Safety: A Holistic Survey](https://mlsanity.com/paper/2412.17686/) (2024)
- [HoneyTrap: Deceiving Large Language Model Attackers to Honeypot Traps with Resilient Multi-Agent Defense](https://mlsanity.com/paper/2601.04034/) (2026)
- [Jailbreak Transferability Emerges from Shared Representations](https://mlsanity.com/paper/2506.12913/) (2025)
- [On Surjectivity of Neural Networks: Can you elicit any behavior from your model?](https://mlsanity.com/paper/2508.19445/) (2025)
- [Stateful Cooperative Agents Safeguarding LLMs Against Evolving Multi-Turn Attacks](https://mlsanity.com/paper/2608.00134/) (2026)

## Abstract (arXiv)

Large language models have drawn significant attention to the challenge of safe alignment, especially regarding jailbreak attacks that circumvent security measures to produce harmful content. To address the limitations of existing methods like GCG, which perform well in single-model attacks but lack transferability, we propose several enhancements, including a scenario induction template, optimized suffix selection, and the integration of re-suffix attack mechanism to reduce inconsistent outputs. Our approach has shown superior performance in extensive experiments across various benchmarks, achieving nearly 100% success rates in both attack execution and transferability. Notably, our method has won the first place in the AISG-hosted Global Challenge for Safe and Secure LLMs. The code is released at https://github.com/HqingLiu/SI-GCG.