This research presents a comprehensive safety testing framework for large language models prior to their public deployment, using OpenAI's o3-mini as a case study.

• Evaluates LLM safety across privacy concerns, biases, and misinformation risks
• Demonstrates the importance of external, independent safety testing
• Identifies specific vulnerabilities that could lead to harmful outputs
• Provides actionable protocols for responsible AI deployment

For security professionals, this research offers crucial insights into how pre-emptive testing can mitigate risks before AI systems reach users, establishing more robust safety standards for the industry.

Early External Safety Testing of OpenAI's o3-mini: Insights from the Pre-Deployment Evaluation