# KnowPhish: Large Language Models Meet Multimodal Knowledge Graphs for Enhancing Reference-Based Phishing Detection

arXiv: https://arxiv.org/abs/2403.02253 · HTML: https://mlsanity.com/paper/2403.02253/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 64 references that are in the map.
- First posted on arXiv 4 Mar 2024.

## Builds on (most cited first, 29 of 29)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories](https://mlsanity.com/paper/2212.10511/) (2022)
- [A Survey on Multimodal Large Language Models](https://mlsanity.com/paper/2306.13549/) (2023)
- [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://mlsanity.com/paper/2306.08302/) (2023)
- [Baseline Defenses for Adversarial Attacks Against Aligned Language Models](https://mlsanity.com/paper/2309.00614/) (2023)
- [Large Language Models Struggle to Learn Long-Tail Knowledge](https://mlsanity.com/paper/2211.08411/) (2022)
- [Detecting Language Model Attacks with Perplexity](https://mlsanity.com/paper/2308.14132/) (2023)
- [Trusting Your Evidence: Hallucinate Less with Context-aware Decoding](https://mlsanity.com/paper/2305.14739/) (2023)
- [A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models](https://mlsanity.com/paper/2401.01313/) (2024)
- [Internet-Augmented Dialogue Generation](https://mlsanity.com/paper/2107.07566/) (2021)
- [Head-to-Tail: How Knowledgeable are Large Language Models (LLMs)? A.K.A. Will LLMs Replace Knowledge Graphs?](https://mlsanity.com/paper/2308.10168/) (2023)
- [Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning](https://mlsanity.com/paper/2305.19523/) (2023)
- [A Survey of Knowledge Enhanced Pre-trained Language Models](https://mlsanity.com/paper/2211.05994/) (2022)
- [Tranco: A Research-Oriented Top Sites Ranking Hardened Against Manipulation](https://mlsanity.com/paper/1806.01156/) (2018)
- [Sticking to the Facts: Confident Decoding for Faithful Data-to-Text Generation](https://mlsanity.com/paper/1910.08684/) (2019)
- [KnowledGPT: Enhancing Large Language Models with Retrieval and Storage Access on Knowledge Bases](https://mlsanity.com/paper/2308.11761/) (2023)
- [Exploring OCR Capabilities of GPT-4V(ision) : A Quantitative and In-depth Evaluation](https://mlsanity.com/paper/2310.16809/) (2023)
- [URLNet: Learning a URL Representation with Deep Learning for Malicious URL Detection](https://mlsanity.com/paper/1802.03162/) (2018)
- [Targeted Phishing Campaigns using Large Scale Language Models](https://mlsanity.com/paper/2301.00665/) (2022)
- [URLTran: Improving Phishing URL Detection Using Transformers](https://mlsanity.com/paper/2106.05256/) (2021)
- [From Chatbots to PhishBots? -- Preventing Phishing scams created using ChatGPT, Google Bard and Claude](https://mlsanity.com/paper/2310.19181/) (2023)
- [Lateral Phishing With Large Language Models: A Large Organization Comparative Study](https://mlsanity.com/paper/2401.09727/) (2024)
- [Phishing Detection Leveraging Machine Learning and Deep Learning: A Review](https://mlsanity.com/paper/2205.07411/) (2022)

## Built on by (most cited first, 30 of 33)

- [Words or Vision: Do Vision-Language Models Have Blind Faith in Text?](https://mlsanity.com/paper/2503.02199/) (2025)
- [Can Indirect Prompt Injection Attacks Be Detected and Removed?](https://mlsanity.com/paper/2502.16580/) (2025)
- [VPI-Bench: Visual Prompt Injection Attacks for Computer-Use Agents](https://mlsanity.com/paper/2506.02456/) (2025)
- [TopicAttack: An Indirect Prompt Injection Attack via Topic Transition](https://mlsanity.com/paper/2507.13686/) (2025)
- [Combining Knowledge Graphs and Large Language Models](https://mlsanity.com/paper/2407.06564/) (2024)
- [CTINexus: Automatic Cyber Threat Intelligence Knowledge Graph Construction Using Large Language Models](https://mlsanity.com/paper/2410.21060/) (2024)
- [Large Language Models for Security Operations Centers: A Comprehensive Survey](https://mlsanity.com/paper/2509.10858/) (2025)
- [Multimodal Large Language Models for Phishing Webpage Detection and Identification](https://mlsanity.com/paper/2408.05941/) (2024)
- [When Bots Take the Bait: Exposing and Mitigating the Emerging Social Engineering Attack in Web Automation Agent](https://mlsanity.com/paper/2601.07263/) (2026)
- [OCR-APT: Reconstructing APT Stories from Audit Logs using Subgraph Anomaly Detection and LLMs](https://mlsanity.com/paper/2510.15188/) (2025)
- [WARD: Adversarially Robust Defense of Web Agents Against Prompt Injections](https://mlsanity.com/paper/2605.15030/) (2026)
- [Large Language Models for Knowledge Graph Embedding: A Survey](https://mlsanity.com/paper/2501.07766/) (2025)
- [PhishDebate: An LLM-Based Multi-Agent Framework for Phishing Website Detection](https://mlsanity.com/paper/2506.15656/) (2025)
- [Backdoor-Powered Prompt Injection Attacks Nullify Defense Methods](https://mlsanity.com/paper/2510.03705/) (2025)
- [WebAgentGuard: A Reasoning-Driven Guard Model for Detecting Prompt Injection Attacks in Web Agents](https://mlsanity.com/paper/2604.12284/) (2026)
- [PhreshPhish: A Real-World, High-Quality, Large-Scale Phishing Website Dataset and Benchmark](https://mlsanity.com/paper/2507.10854/) (2025)
- [PreScam: A Benchmark for Predicting Scam Progression from Early Conversations](https://mlsanity.com/paper/2605.12243/) (2026)
- [Cyri: A Conversational AI-based Assistant for Supporting the Human User in Detecting and Responding to Phishing Attacks](https://mlsanity.com/paper/2502.05951/) (2025)
- [WebGuard++:Interpretable Malicious URL Detection via Bidirectional Fusion of HTML Subgraphs and Multi-Scale Convolutional BERT](https://mlsanity.com/paper/2506.19356/) (2025)
- [PiMRef: Deducing Ever-evolving Spear-phishing Emails with Knowledge Base Invariants](https://mlsanity.com/paper/2507.15393/) (2025)
- [Hallucination-Resistant Security Planning with a Large Language Model](https://mlsanity.com/paper/2602.05279/) (2026)
- [MemoPhishAgent: Memory-Augmented Multi-Modal LLM Agent for Phishing URL Detection](https://mlsanity.com/paper/2602.21394/) (2026)
- [Phishing the Phishers with SpecularNet: Hierarchical Graph Autoencoding for Reference-Free Web Phishing Detection](https://mlsanity.com/paper/2603.01874/) (2026)
- [CyberAId: AI-Driven Cybersecurity for Financial Service Providers](https://mlsanity.com/paper/2605.01892/) (2026)
- [AliMark: Enhancing Robustness of Sentence-Level Watermarking Against Text Paraphrasing](https://mlsanity.com/paper/2605.29434/) (2026)
- [Position Paper: Think Globally, React Locally -- Bringing Real-time Reference-based Website Phishing Detection on macOS](https://mlsanity.com/paper/2405.18236/) (2024)
- [Adaptive Linguistic Prompting (ALP) Enhances Phishing Webpage Detection in Multimodal Large Language Models](https://mlsanity.com/paper/2507.13357/) (2025)
- [PhishLumos: An Adaptive Multi-Agent System for Proactive Phishing Campaign Mitigation](https://mlsanity.com/paper/2509.21772/) (2025)
- [Role-Aware Multi-modal federated learning system for detecting phishing webpages](https://mlsanity.com/paper/2509.22369/) (2025)
- [An LLM Agent-based Framework for Whaling Countermeasures](https://mlsanity.com/paper/2601.14606/) (2026)

## Abstract (arXiv)

Phishing attacks have inflicted substantial losses on individuals and businesses alike, necessitating the development of robust and efficient automated phishing detection approaches. Reference-based phishing detectors (RBPDs), which compare the logos on a target webpage to a known set of logos, have emerged as the state-of-the-art approach. However, a major limitation of existing RBPDs is that they rely on a manually constructed brand knowledge base, making it infeasible to scale to a large number of brands, which results in false negative errors due to the insufficient brand coverage of the knowledge base. To address this issue, we propose an automated knowledge collection pipeline, using which we collect a large-scale multimodal brand knowledge base, KnowPhish, containing 20k brands with rich information about each brand. KnowPhish can be used to boost the performance of existing RBPDs in a plug-and-play manner. A second limitation of existing RBPDs is that they solely rely on the image modality, ignoring useful textual information present in the webpage HTML. To utilize this textual information, we propose a Large Language Model (LLM)-based approach to extract brand information of webpages from text. Our resulting multimodal phishing detection approach, KnowPhish Detector (KPD), can detect phishing webpages with or without logos. We evaluate KnowPhish and KPD on a manually validated dataset, and a field study under Singapore's local context, showing substantial improvements in effectiveness and efficiency compared to state-of-the-art baselines.