Last analyzed Sep 9, 2026

Language Model Security Database

Explore primary-source AI security research, evaluated models, attack techniques, and defensive evidence.

985 research findings · 1119 evaluated models

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131 entries

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Code-Switching LLM Jailbreak

Published Jun 17, 2024Analyzed Dec 28, 2024Source: arXiv

Large Language Models (LLMs) exhibit increased vulnerability to adversarial prompts employing code-switching techniques, where multiple languages are interwoven within a single query. This vulnerability stems from an unintended correlation between the resource availability of the languages used in the prompt and the LLM's safety alignment. LLMs trained on imbalanced multilingual data are more susceptible to attacks leveraging low-resource languages, resulting in a higher rate of unsafe or…

Code-Switching Red-Teaming: LLM Evaluation for Safety and Multilingual Understanding

Evaluated models: Not reported

Research methodology

Entries summarize publicly available primary-source security research. Model names reflect only systems explicitly evaluated by the cited paper, and measurements are research-reported unless independent verification is stated.

Discovery and publication are separate: new papers enter a candidate queue, then require human review and explicit approval before publication. Updates follow that review process, not a guaranteed daily publishing schedule. “Last analyzed” is the newest analysis date among published entries; it does not mean that all recent research has been covered.