This research investigates how Large Language Models can be compromised when deployed for spam detection, revealing critical security implications.

• LLMs fine-tuned for spam detection are vulnerable to adversarial attacks
• Spammers can bypass detection by using specific text patterns that confuse the models
• Research identifies data poisoning as a significant threat to LLM-based security systems
• Findings highlight the need for robust defenses against evolving spam tactics

For security professionals, this work demonstrates the importance of understanding AI vulnerabilities before deployment in production environments, especially for critical content filtering applications.

An Investigation of Large Language Models and Their Vulnerabilities in Spam Detection