mlsanityData to 14 Oct 2026

PhishLumos: An Adaptive Multi-Agent System for Proactive Phishing Campaign Mitigation

arXiv 2509.21772 ↗First posted 26 Sep 2025cs.CRcited by 0

On the map: LLM agents and tool use → Agent threats, guardrails and governance

Abstract (arXiv)

Phishing attacks are a significant societal threat, disproportionately harming vulnerable populations and eroding trust in essential digital services. Current defenses are often reactive, failing against modern evasive tactics like cloaking that conceal malicious content. To address this, we introduce PhishLumos, an adaptive multi-agent system that proactively mitigates entire attack campaigns. It confronts a core cybersecurity imbalance: attackers can easily scale operations, while defense remains an intensive expert task. Instead of being blocked by evasion, PhishLumos treats it as a critical signal to investigate the underlying infrastructure. Its Large Language Model (LLM)-powered agents uncover shared hosting, certificates, and domain registration patterns. On real-world data, our system identified 100% of campaigns in the median case, over a week before their confirmation by cybersecurity experts. PhishLumos demonstrates a practical shift from reactive URL blocking to proactive campaign mitigation, protecting users before they are harmed and making the digital world safer for all.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 12 of its 58 references that are in the map. First posted on arXiv 26 Sep 2025.

Builds on

12 of 58 references.

References · 12

  1. 2018Tranco: A Research-Oriented Top Sites Ranking Hardened Against Manipulationcited by 63
  2. 2024KnowPhish: Large Language Models Meet Multimodal Knowledge Graphs for Enhancing Reference-Based Phishing Detectioncited by 33
  3. 2024ChatSpamDetector: Leveraging Large Language Models for Effective Phishing Email Detectioncited by 25
  4. 2024PhishAgent: A Robust Multimodal Agent for Phishing Webpage Detectioncited by 19
  5. 2022Phishing URL Detection: A Network-based Approach Robust to Evasioncited by 7
  6. 2019ShamFinder: An Automated Framework for Detecting IDN Homographscited by 3
  7. 2025PhishParrot: LLM-Driven Adaptive Crawling to Unveil Cloaked Phishing Sitescited by 3
  8. 2023Canary in Twitter Mine: Collecting Phishing Reports from Experts and Non-expertscited by 2
  9. 2023PhishReplicant: A Language Model-based Approach to Detect Generated Squatting Domain Namescited by 1
  10. 2024DomainLynx: Leveraging Large Language Models for Enhanced Domain Squatting Detectioncited by 1
  11. 2024DomainHarvester: Harvesting Infrequently Visited Yet Trustworthy Domain Namescited by 1
  12. 2025ScamFerret: Detecting Scam Websites Autonomously with Large Language Modelscited by 1

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  1. 2025PhishDebate: An LLM-Based Multi-Agent Framework for Phishing Website Detection
  2. 2025MultiPhishGuard: An Explainable and Adaptive Multi-Agent LLM System for Phishing Email Detection
  3. 2025PhishIntentionLLM: Uncovering Phishing Website Intentions through Multi-Agent Retrieval-Augmented Generation
  4. 2025CLASP: Cost-Optimized LLM-based Agentic System for Phishing Detection
  5. 2024Automated Phishing Detection Using URLs and Webpages
  6. 2025EvoMail: Self-Evolving Cognitive Agents for Adaptive Spam and Phishing Email Defense
  7. 2026Can LLM Agents Simulate Dynamic Networks? A Case Study on Email Networks with Phishing Synthesis
  8. 2024Large Multimodal Agents for Accurate Phishing Detection with Enhanced Token Optimization and Cost Reduction