ActiveFence's emerging threats assessment analyzed seven major large language models (LLMs) across multiple abuse categories: hate speech, disinformation, fraud, and child safety-related prompts. The research revealed that 44% of the LLM outputs were classified as risky, with a significant majority (68%) of unsafe outputs linked to hate speech. Fraud-related outputs were relatively better controlled, but the models showed substantial deficiencies in mitigating hate speech and child safety risks. The study underscores that no tested LLM achieved a safe rating, indicating systemic vulnerabilities inherent in current AI language models. These vulnerabilities arise from the models' training data, prompt engineering weaknesses, and insufficient content moderation mechanisms, allowing adversaries to exploit them to generate harmful or misleading content. Unlike traditional software vulnerabilities, these are behavioral and content-generation risks that can be triggered by crafted user inputs without requiring system-level exploits or authentication. The findings highlight the need for ongoing evaluation, red-teaming, and external collaboration to monitor and mitigate emerging threats related to LLM misuse. This threat affects organizations deploying or relying on LLMs for content generation, customer interaction, or decision support, especially where regulatory compliance around hate speech and child protection is critical.

For European organizations, the impact of these LLM vulnerabilities is multifaceted. Exposure to hate speech and disinformation can damage brand reputation, lead to regulatory penalties under laws such as the EU Digital Services Act, and cause societal harm. Child safety-related content generation risks can trigger legal liabilities and undermine trust in AI-powered services. Fraud-related vulnerabilities, though less prevalent, still pose risks to financial and transactional integrity. Organizations in sectors like media, education, social platforms, and public services that use LLMs for content moderation, customer engagement, or automated communication are particularly vulnerable. The propagation of harmful content can also exacerbate social tensions and attract negative media attention. Additionally, failure to address these risks may hinder compliance with emerging AI regulations in Europe, potentially resulting in fines or operational restrictions. The threat also challenges the integrity and reliability of AI outputs, affecting decision-making processes and user trust.

European organizations should implement multi-layered mitigation strategies tailored to LLM-specific risks. First, integrate advanced content filtering and moderation tools that specialize in detecting hate speech and child safety violations, ideally leveraging external threat intelligence providers like ActiveFence. Second, conduct continuous red-teaming exercises focusing on prompt injection and adversarial input testing to identify and patch model weaknesses. Third, apply prompt engineering best practices to limit the generation of unsafe content, including the use of guardrails and context-aware filters. Fourth, maintain transparency with users about AI limitations and implement feedback mechanisms to report harmful outputs. Fifth, ensure compliance with EU AI regulations by documenting risk assessments and mitigation efforts. Finally, collaborate with external researchers and industry groups to stay informed about emerging threats and share best practices. Avoid reliance on a single vendor or model; consider ensemble approaches or fallback mechanisms to reduce risk exposure.