mlsanityData to 14 Oct 2026

Can MLLMs Detect Phishing? A Comprehensive Security Benchmark Suite Focusing on Dynamic Threats and Multimodal Evaluation in Academic Environments

arXiv 2511.15165 ↗First posted 19 Nov 2025cs.CR cs.AIcited by 0

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Abstract (arXiv)

The rapid proliferation of Multimodal Large Language Models (MLLMs) has introduced unprecedented security challenges, particularly in phishing detection within academic environments. Academic institutions and researchers are high-value targets, facing dynamic, multilingual, and context-dependent threats that leverage research backgrounds, academic collaborations, and personal information to craft highly tailored attacks. Existing security benchmarks largely rely on datasets that do not incorporate specific academic background information, making them inadequate for capturing the evolving attack patterns and human-centric vulnerability factors specific to academia. To address this gap, we present AdapT-Bench, a unified methodological framework and benchmark suite for systematically evaluating MLLM defense capabilities against dynamic phishing attacks in academic settings.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 17 references that are in the map. First posted on arXiv 19 Nov 2025.

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8 of 17 references.

References · 8

  1. 2023Universal and Transferable Adversarial Attacks on Aligned Language Modelscited by 3,104
  2. 2023MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGIcited by 2,080
  3. 2023GPTFUZZER: Red Teaming Large Language Models with Auto-Generated Jailbreak Promptscited by 560
  4. 2023mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality Collaborationcited by 547
  5. 2024Distract Large Language Models for Automatic Jailbreak Attackcited by 45
  6. 2024Jailbreaking Large Language Models with Symbolic Mathematicscited by 16
  7. 2024Can MLLMs Understand the Deep Implication Behind Chinese Images?cited by 14
  8. 2025xJailbreak: Representation Space Guided Reinforcement Learning for Interpretable LLM Jailbreakingcited by 11

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  1. 2025Adaptive Linguistic Prompting (ALP) Enhances Phishing Webpage Detection in Multimodal Large Language Models
  2. 2025Evaluating Large Language Models for Phishing Detection, Self-Consistency, Faithfulness, and Explainability
  3. 2026Clouding the Mirror: Stealthy Prompt Injection Attacks Targeting LLM-based Phishing Detection
  4. 2026GuardPhish: Securing Open-Source LLMs from Phishing Abuse
  5. 2025Phishing Detection in the Gen-AI Era: Quantized LLMs vs Classical Models
  6. 2025Phishing Email Detection Using Large Language Models
  7. 2026User-Centric Phishing Detection: A RAG and LLM-Based Approach ↗
  8. 2024Multimodal Large Language Models for Phishing Webpage Detection and Identification