Evolving Jailbreaks: Automated Multi-Objective Long-Tail Attacks on Large Language Models
Large Language Models (LLMs) have been widely deployed, especially through free Web-based applications that expose them to diverse user-generated inputs, including those from long-tail distributions such as low-resource languages and encrypted private data. This open-ended exposure increases the risk of jailbreak attacks that undermine model safety alignment. While recent studies have shown that leveraging long-tail distributions can facilitate such jailbreaks, existing approaches largely rely on handcrafted rules, limiting the systematic evaluation of these security and privacy vulnerabilities. In this work, we present EvoJail, an automated framework for discovering long-tail distributio...
| Authors | Wenjing Hong, Zhonghua Rong, Li Wang et al. |
|---|---|
| Primary category | cs.CR |
| Published | 20 March 2026 |
| Last updated at source | 20 March 2026 |
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