Gesamte Forschung

OpenAI recently introduced ChatGPT agent. What are its capabilities and the associated cybersecurity risks? We examined these using Trend Micro’s Digital Assistant Framework to help organizations navigate the changes and risks this new digital assistant may bring.

As agentic AI systems grow increasingly complex, it becomes clear that this class of applications relies on a multi-layered architecture. Trying to chart such architecture reveals several security risks that could plague each layer. This article investigates the possible scenarios and offers actionable insights to secure each layer and combat such threats.

As open-source AI models become foundational to digital infrastructure, hidden backdoors and tampered supply chains pose a growing, under-recognized threat that traditional security tools can fail to detect.

This report takes a comprehensive look at how deepfakes are used to support criminal business processes, what are the toolkits criminals are exploiting to power their deepfake creation, and what the deepfake underground looks like.

Cyberattacks are growing increasingly sophisticated and frequent, which is why security strategies focused solely on detection and response are no longer sufficient. This reality brings renewed attention to the importance of identifying and mitigating cyber risks before incidents occur.

Over the years, Trend Micro researchers have published articles and research papers that detail different criminal underground communities around the world. Read about their motives, ecosystems, business models, and techniques to anticipate and proactively counter threats before they strike.

Our latest research provides a framework for understanding agentic AI systems, outlines their core characteristics, and examines the security implications surrounding their use.

Trend Vision One™ tackles 7 of OWASP’s Top 10 LLM vulnerabilities, offering comprehensive protection against prompt injection, data leakage, AI supply chain risks, and other critical flaws.

Our research examines how AI coding assistants can hallucinate plausible but non-existent package names—therefore enabling slopsquatting attacks—while also providing practical defense strategies that organizations can implement to secure their development pipelines