# InfoFlood: Jailbreaking Large Language Models with Information Overload

**Authors:** Advait Yadav, Haibo Jin, Man Luo, Jun Zhuang, Haohan Wang  
**arXiv:** [2506.12274](https://arxiv.org/abs/2506.12274)  
**Date:** 2025-06-13  
**Summary page:** https://www.summarizepaper.com/en/paper/2506.12274v1/

## Key points

- Large Language Models (LLMs) are vulnerable to adversarial attacks
- A novel technique called Information Overload exploits excessive linguistic complexity within queries to disrupt safety mechanisms directly
- InfoFlood is a sophisticated jailbreak attack that transforms malicious queries into complex prompts, outperforming baseline attacks on popular LLMs like GPT-4o and Gemini 2.0
- Existing post-processing defenses have limitations in mitigating Information Overload-based attacks
- The study emphasizes the importance of robust defenses against advanced jailbreak strategies like InfoFlood in the evolving landscape of AI security

## Summary

This study delves into the vulnerability of Large Language Models (LLMs) to adversarial attacks and introduces a novel technique called Information Overload. Unlike traditional jailbreak methods, which involve adding specific prefixes or suffixes to prompts, Information Overload exploits excessive linguistic complexity within queries to directly disrupt safety mechanisms. The researchers present InfoFlood as a sophisticated jailbreak attack that transforms malicious queries into complex prompts capable of evading built-in safeguards. Through empirical validation on popular LLMs like GPT-4o and Gemini 2.0, InfoFlood consistently outperforms baseline attacks in achieving successful jailbreaks. The study also highlights the limitations of existing post-processing defenses in mitigating Information Overload-based attacks. These findings underscore the need for robust defenses against emerging threats posed by advanced jailbreak strategies like InfoFlood and shed light on the evolving landscape of AI security. are vulnerable to , particularly a new technique called . This exploit takes advantage of excessive linguistic complexity within queries to bypass safety mechanisms directly instead of using traditional methods such as adding prefixes or suffixes. To demonstrate this vulnerability, the researchers introduce , a sophisticated jailbreak attack that transforms malicious queries into complex prompts capable of evading built-in safeguards. They analyze failed transformations using the Rejection Analysis agent and employ Saturation Refinement to fine-tune unsuccessful attempts while preserving the core malicious intent. Empirical validation on popular LLMs shows that InfoFlood consistently outperforms baseline attacks in achieving successful jailbreaks. Furthermore, the study highlights the limitations of existing post-processing defenses in mitigating Information Overload-based attacks and emphasizes the need for robust defenses against emerging threats in the evolving landscape of AI security.