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A Cross-Language Investigation into Jailbreak Attacks in Large Language Models
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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 14 Pith papers
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TukaBench: A Culturally Grounded Jailbreak Benchmark for African Languages
TukaBench extends JailbreakBench to African languages via human translation, cultural adaptation, curated prompts, and code-switching, finding lower refusal rates for culturally grounded prompts and surfacing comprehe...
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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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Multilingual Safety Alignment via Self-Distillation
MSD enables cross-lingual safety transfer in LLMs via self-distillation with Dual-Perspective Safety Weighting, improving safety in low-resource languages without target response data.
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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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Multilingual Safety Alignment via Self-Distillation
MSD transfers LLM safety from high-resource to low-resource languages via self-distillation and dual-perspective weighting without needing response data.
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Cross-Lingual Jailbreak Detection via Semantic Codebooks
Semantic similarity to an English jailbreak codebook detects cross-lingual attacks with high accuracy on curated benchmarks but shows poor separability on diverse unsafe prompts.
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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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One Jailbreak, Many Tongues: Learning Language-Insensitive Intention Representations for Multilingual Jailbreak Detection
MLJailDe achieves 98.5% F1 on multilingual jailbreak detection by combining back-translation data augmentation, supervised contrastive loss, and imbalance-aware classification on a DeBERTa backbone.
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Sentra-Guard: A Real-Time Multilingual Defense Against Adversarial LLM Prompts
Sentra-Guard reports 99.96% detection of adversarial LLM prompts with AUC 1.00 and ASR of 0.004% using a hybrid SBERT-FAISS and transformer classifier architecture with multilingual translation and human feedback.
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Jailbreak Attacks and Defenses Against Large Language Models: A Survey
A survey that creates taxonomies for jailbreak attacks and defenses on LLMs, subdivides them into sub-classes, and compares evaluation approaches.
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Safety at Scale: A Comprehensive Survey of Large Model and Agent Safety
A comprehensive survey that taxonomizes safety threats to large models and agents, reviews defenses and benchmarks, and outlines open challenges.
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