Protect your AI applications and agents from attacks, fakes, unauthorized access, and malicious data inputs.
Control your GenAI applications and agents and assure their alignment with their business purpose.
Proactively test GenAI models, agents, and applications before attackers or users do
The only real-time multi-language multimodality technology to ensure your brand safety and alignment with your GenAI applications.
Ensure your app is compliant with changing regulations around the world across industries.
Proactively identify vulnerabilities through red teaming to produce safe, secure, and reliable models.
Detect and prevent malicious prompts, misuse, and data leaks to ensure your conversational AI remains safe, compliant, and trustworthy.
Protect critical AI-powered applications from adversarial attacks, unauthorized access, and model exploitation across environments.
Provide enterprise-wide AI security and governance, enabling teams to innovate safely while meeting internal risk standards.
Safeguard user-facing AI products by blocking harmful content, preserving brand reputation, and maintaining policy compliance.
Secure autonomous agents against malicious instructions, data exfiltration, and regulatory violations across industries.
Ensure hosted AI services are protected from emerging threats, maintaining secure, reliable, and trusted deployments.
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, ActiveFenceโs 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 LLMโs 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.
ActiveFenceโs 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.