Large Language Models (LLMs) have transformed artificial intelligence by advancing
natural language understanding and generation, enabling applications across fields beyond …

Large Language Models (LLMs) demonstrate remarkable capabilities across diverse
applications. However, concerns regarding their security, particularly the vulnerability to …

This study exposes the safety vulnerabilities of Large Language Models (LLMs) in multi-turn
interactions, where malicious users can obscure harmful intents across several queries. We …

Large language models are susceptible to jailbreak attacks, which can result in the
generation of harmful content. While prior defenses mitigate these risks by perturbing or …

Large language models (LLMs) have exhibited outstanding performance in engaging with
humans and addressing complex questions by leveraging their vast implicit knowledge and …

Improving the safety and reliability of large language models (LLMs) is a crucial aspect of
realizing trustworthy AI systems. Although alignment methods aim to suppress harmful …

The existing safety alignment of Large Language Models (LLMs) is found fragile and could
be easily attacked through different strategies, such as through fine-tuning on a few harmful …

Large language models are susceptible to jailbreak attacks, which can result in the
generation of harmful content. While prior defenses mitigate these risks by perturbing or …

Large language models (LLMs) have succeeded significantly in various applications but
remain susceptible to adversarial jailbreaks that void their safety guardrails. Previous …

As large language models (LLMs) become more integrated into everyday applications,
ensuring their robustness and security is increasingly critical. In particular, LLMs can be …