Automated red teaming holds substantial promise for uncovering and mitigating the risks
associated with the malicious use of large language models (LLMs), yet the field lacks a …

As large language models (LLMs) become increasingly prevalent across many real-world
applications, understanding and enhancing their robustness to user inputs is of paramount …

In response to rising concerns surrounding the safety, security, and trustworthiness of
Generative AI (GenAI) models, practitioners and regulators alike have pointed to AI red …

Safety is critical to the usage of large language models (LLMs). Multiple techniques such as
data filtering and supervised fine-tuning have been developed to strengthen LLM safety …

Independent evaluation and red teaming are critical for identifying the risks posed by
generative AI systems. However, the terms of service and enforcement strategies used by …

The rapid evolution of artificial intelligence (AI) through developments in Large Language
Models (LLMs) and Vision-Language Models (VLMs) has brought significant advancements …

Large language models (LLMs) have significantly transformed the landscape of Natural
Language Processing (NLP). Their impact extends across a diverse spectrum of tasks …

In the burgeoning field of Large Language Models (LLMs), developing a robust safety
mechanism, colloquially known as" safeguards" or" guardrails", has become imperative to …

Large Language Models (LLMs) have performed exceptionally in various text-generative
tasks, including question answering, translation, code completion, etc. However, the over …

Manual Red teaming is a commonly-used method to identify vulnerabilities in large
language models (LLMs), which, is costly and unscalable. In contrast, automated red …