  Phishing detection is a critical cybersecurity task that involves the
identification and neutralization of fraudulent attempts to obtain sensitive
information, thereby safeguarding individuals and organizations from data
breaches and financial loss. In this project, we address the constraints of
traditional reference-based phishing detection by developing an LLM agent
framework. This agent harnesses Large Language Models to actively fetch and
utilize online information, thus providing a dynamic reference system for more
accurate phishing detection. This innovation circumvents the need for a static
knowledge base, offering a significant enhancement in adaptability and
efficiency for automated security measures.
  The project report includes an initial study and problem analysis of existing
solutions, which motivated us to develop a new framework. We demonstrate the
framework with LLMs simulated as agents and detail the techniques required for
construction, followed by a complete implementation with a proof-of-concept as
well as experiments to evaluate our solution's performance against other
similar solutions. The results show that our approach has achieved with
accuracy of 0.945, significantly outperforms the existing solution(DynaPhish)
by 0.445. Furthermore, we discuss the limitations of our approach and suggest
improvements that could make it more effective.
  Overall, the proposed framework has the potential to enhance the
effectiveness of current reference-based phishing detection approaches and
could be adapted for real-world applications.
