Proactively identify vulnerabilities through red teaming to produce safe, secure, and reliable models.
Deploy generative AI applications and agents in a safe, secure, and scalable way with guardrails.
GenAI tools, and the Large Language Models (LLMs) that underpin them – are impacting the day-to-day lives of billions of users across the globe. But can these technologies be trusted to keep users safe?
This report examines how this new technology can be used by bad actors and vulnerable users to create dangerous content. By testing LLM responses to risky prompts, we are able to assess their relative safety, identify weaknesses, and, most importantly – define actionable steps to improve LLM safety.
In this first independent benchmarking report into the LLM safety landscape, ActiveFence’s subject-matter experts put LLMs to the test. We ran over 20,000 prompts to analyze the responses of six leading LLMs in seven major languages, across four high-risk abuse areas:
The results offer important data for teams to understand their LLM’s relative strengths and weaknesses, and understand where resource allocation is required.
Uncover key trends in AI-enabled online child abuse and learn strategies to detect, prevent, and respond to these threats.
ActiveFence’s annual State of Trust & Safety report uncovers the unique threats and challenges facing Trust & Safety teams during this complex year.
Uncover five essential red teaming tactics to fortify your GenAI systems against misuse and vulnerabilities.