@inproceedings{chen-etal-2026-memophishagent,
    title = "{M}emo{P}hish{A}gent: Memory-Augmented Multi-Modal {LLM} Agent for Phishing {URL} Detection",
    author = "Chen, Xuan  and
      Liu, Hao  and
      Yuan, Tao  and
      Kafai, Mehran  and
      Habas, Piotr  and
      Zhang, Xiangyu",
    editor = "Li, Yunyao  and
      Rehm, Georg  and
      Tu, Mei",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-industry.84/",
    pages = "1182--1196",
    ISBN = "979-8-89176-394-4",
    abstract = "Traditional phishing website detection relies on static heuristics or reference lists, which lag behind rapidly evolving attacks. While recent systems incorporate large language models (LLMs), they are still prompt-based, deterministic pipelines that underutilize reasoning capability.We present MemoPhishAgent (MPA), a memory-augmented multi-modal LLM agent that dynamically orchestrates phishing-specific tools and leverages episodic memories of past reasoning trajectories to guide decisions on recurring and novel threats.On two public datasets, MPA outperforms three state-of-the-art (SOTA) baselines, improving recall by 13.6{\%}.To better reflect realistic, user-facing phishing detection performance, we further evaluate MPA on a benchmark of real-world suspicious URLs actively crawled from five social media platforms, where it improves recall by 20{\%}.Detailed analysis shows episodic memory contributes up to 27{\%} recall gain without introducing additional computational overhead.The ablation study confirms the necessity of the agent-based approach compared to prompt-based baselines and validates the effectiveness of our tool design.Finally, MPA is deployed in production, processing $\sim60K$ targeted high-risk URLs weekly, and achieving 91.44{\%} recall, providing proactive protection for millions of customers.Together, our results show that combining multi-modal reasoning with episodic memory yields robust, adaptable phishing detection in realistic user-exposure settings.Our implementation is available at \url{https://github.com/XuanChen-xc/MemoPhishAgent.git}."
}