This research introduces DetectRL, a new benchmark revealing that even state-of-the-art LLM text detectors underperform in practical applications.

• Current detection methods show impressive results in lab settings but struggle with real-world text samples
• The benchmark covers domains highly susceptible to AI misuse (education, news, finance)
• Tests reveal significant performance gaps between controlled and real-world scenarios
• Provides insights for developing more robust detection systems for security applications

For security professionals, this work highlights critical vulnerabilities in our ability to detect AI-generated content that could be used for misinformation, academic dishonesty, or other harmful purposes.

DetectRL: Benchmarking LLM-Generated Text Detection in Real-World Scenarios