AI-POWERED DEFENSE AGAINST SQL INJECTION, XSS, AND BRUTE-FORCE ATTACKS

Abstrak:

The rising complexity of web applications has made them increasingly vulnerable to sophisticated cyberattacks such as SQL Injection (SQLi), Cross-Site Scripting (XSS), and brute-force login attempts. While traditional security mechanisms remain essential, recent advances in artificial intelligence (AI), particularly large language models like ChatGPT, offer new possibilities for both prevention and detection. This paper explores the integration of ChatGPT into secure coding practices and penetration testing workflows. We demonstrate how ChatGPT can be prompted to detect insecure coding patterns, suggest secure alternatives, and simulate attack payloads for testing. The paper also presents comparative results from using ChatGPT to detect and patch sample SQLi, XSS, and brute-force vulnerabilities in Python-based web applications. Our findings suggest that combining AI with conventional security frameworks significantly reduces vulnerability exposure and increases system resilience.