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A Cross-Language Investigation into Jailbreak Attacks in Large Language Models
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Large Language Models (LLMs) have become increasingly popular for their advanced text generation capabilities across various domains. However, like any software, they face security challenges, including the risk of 'jailbreak' attacks that manipulate LLMs to produce prohibited content. A particularly underexplored area is the Multilingual Jailbreak attack, where malicious questions are translated into various languages to evade safety filters. Currently, there is a lack of comprehensive empirical studies addressing this specific threat. To address this research gap, we conducted an extensive empirical study on Multilingual Jailbreak attacks. We developed a novel semantic-preserving algorithm to create a multilingual jailbreak dataset and conducted an exhaustive evaluation on both widely-used open-source and commercial LLMs, including GPT-4 and LLaMa. Additionally, we performed interpretability analysis to uncover patterns in Multilingual Jailbreak attacks and implemented a fine-tuning mitigation method. Our findings reveal that our mitigation strategy significantly enhances model defense, reducing the attack success rate by 96.2%. This study provides valuable insights into understanding and mitigating Multilingual Jailbreak attacks.
Forward citations
Cited by 8 Pith papers
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Single Canonical Prompts Underestimate LLM Safety's Surface-Form Sensitivity
Evaluating LLM safety with one canonical prompt understates unsafe behavior; across five meaning-preserving reformulations, 5-13% of safe-on-canonical seeds become unsafe, and the union exceeds the worst single form f...
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SafeFlow: Semantic Information-Flow Control for Blocking Malicious Propagation in Multi-Agent Systems
A workflow-level taint-propagation defense blocks fragmented malicious multi-agent workflows, cutting average attack success from 69.3% to 12.7% on four benchmarks.
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SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses
A systemization of LLM jailbreak security that adds linked taxonomies, an evaluation platform, and JailbreakDB, while its main attack–defense comparison results remain deferred.
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When Smiley Turns Hostile: Interpreting How Emojis Trigger LLMs' Toxicity
Emojis in harmful prompts bypass LLM safety more effectively than plain text, across 7 models and 5 languages, through a heterogeneous tokenization channel.
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LLM in the Middle: A Systematic Review of Threats and Mitigations to Real-World LLM-based Systems
A systematic review that categorizes LLM threats, severity scores, and mitigations across development and operation life cycles and multiple deployment scenarios.
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Strategic Deflection: Defending LLMs from Logit Manipulation
SDeflection, a preference-trained defense that makes LLMs deflect harmful requests into benign answers, cuts ASR of a logit-manipulation attack from 89-95% to 8-35% on Llama-2, Llama-3, and Mistral.
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MPO: Multilingual Safety Alignment via Reward Gap Optimization
MPO reduces jailbreak success in multilingual LLMs by regressing target-language reward gaps onto the English reward gap, outperforming DPO and related methods while preserving utility.
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Layer-Wise Perturbations via Sparse Autoencoders for Adversarial Text Generation
Sparse autoencoder activation perturbation (SFPF) applied on top of existing jailbreak prompts raises attack success rate on Qwen3-32B, but with no defense evaluation and weak reproducibility.
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