What I learned building AI agents for bug bounty hunting

Exploring the integration of AI into an already automated workflow using Bash, the author embarked on a journey to develop multi-agent systems, initially using LangChain and later CrewAI, to search for information disclosure vulnerabilities in PDFs and XLS files indexed by Google. Despite the excitement of creating a functioning AI-driven workflow, the project faced challenges with numerous false positives, prompting a reassessment of which tasks were better suited for traditional automation versus AI. Through this process, the author learned that while AI, particularly large language models (LLMs), can significantly enhance bug bounty hunting by optimizing tasks like code analysis and vulnerability identification, it is not always necessary or superior to established methods. The importance of selecting the right model and understanding the limitations and costs associated with AI were emphasized, alongside the notion that AI should act as an amplifier of existing skills rather than a replacement. The author concluded that human intuition remains indispensable, advocating for a balanced approach that combines AI with human oversight in cybersecurity tasks.