LLM Security
Researchers demonstrate scalable methods for automating multi-turn jailbreak attacks against large language models, revealing critical vulnerabilities in current AI safety measures and guardrails.
AI Safety
New research examines adversarial alignment across multiple language models, revealing how jailbreak attack effectiveness scales with model size and defensive measures. The study provides quantitative insights into LLM security vulnerabilities.
LLM Security
Researchers introduce AdversariaLLM, a modular framework for evaluating large language model vulnerabilities. The open-source toolbox standardizes adversarial testing methodologies for AI security research.