This research systematically evaluates the effectiveness of popular AI-generated text detectors across various conditions, revealing significant practical limitations.

• Performance inconsistency across different domains and language models
• High false positive rates when analyzing human-written text
• Vulnerability to adversarial attacks and simple evasion techniques
• Significant performance degradation when facing content outside training distributions

For security professionals, these findings highlight critical gaps in our defensive capabilities against AI-generated misinformation and impersonation attacks, suggesting the need for more robust, multi-faceted detection approaches rather than relying on any single detector.

A Practical Examination of AI-Generated Text Detectors for Large Language Models