# PiMRef: Deducing Ever-evolving Spear-phishing Emails with Knowledge Base Invariants

arXiv: https://arxiv.org/abs/2507.15393 · HTML: https://mlsanity.com/paper/2507.15393/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 98 references that are in the map.
- First posted on arXiv 21 Jul 2025.

## Builds on (most cited first, 28 of 28)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment](https://mlsanity.com/paper/1907.11932/) (2019)
- [Instruction Tuning for Large Language Models: A Survey](https://mlsanity.com/paper/2308.10792/) (2023)
- [Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers](https://mlsanity.com/paper/1801.04354/) (2018)
- [BAE: BERT-based Adversarial Examples for Text Classification](https://mlsanity.com/paper/2004.01970/) (2020)
- [CharacterBERT: Reconciling ELMo and BERT for Word-Level Open-Vocabulary Representations From Characters](https://mlsanity.com/paper/2010.10392/) (2020)
- [KnowPhish: Large Language Models Meet Multimodal Knowledge Graphs for Enhancing Reference-Based Phishing Detection](https://mlsanity.com/paper/2403.02253/) (2024)
- [ChatSpamDetector: Leveraging Large Language Models for Effective Phishing Email Detection](https://mlsanity.com/paper/2402.18093/) (2024)
- [Measure-Observe-Remeasure: An Interactive Paradigm for Differentially-Private Exploratory Analysis](https://mlsanity.com/paper/2406.01964/) (2024)
- [When LLMs Go Online: The Emerging Threat of Web-Enabled LLMs](https://mlsanity.com/paper/2410.14569/) (2024)
- [CharacterBERT and Self-Teaching for Improving the Robustness of Dense Retrievers on Queries with Typos](https://mlsanity.com/paper/2204.00716/) (2022)
- [Detecting and Characterizing Lateral Phishing at Scale](https://mlsanity.com/paper/1910.00790/) (2019)
- [Lateral Phishing With Large Language Models: A Large Organization Comparative Study](https://mlsanity.com/paper/2401.09727/) (2024)
- [Cognitive Triaging of Phishing Attacks](https://mlsanity.com/paper/1905.02162/) (2019)
- [Multi-SpacePhish: Extending the Evasion-space of Adversarial Attacks against Phishing Website Detectors using Machine Learning](https://mlsanity.com/paper/2210.13660/) (2022)
- [RAIDER: Reinforcement-aided Spear Phishing Detector](https://mlsanity.com/paper/2105.07582/) (2021)
- [Mitigating Adversarial Gray-Box Attacks Against Phishing Detectors](https://mlsanity.com/paper/2212.05380/) (2022)
- [Prompted Contextual Vectors for Spear-Phishing Detection](https://mlsanity.com/paper/2402.08309/) (2024)
- [Content, Nudges and Incentives: A Study on the Effectiveness and Perception of Embedded Phishing Training](https://mlsanity.com/paper/2409.01378/) (2024)
- [Catching the Phish: Detecting Phishing Attacks using Recurrent Neural Networks (RNNs)](https://mlsanity.com/paper/1908.03640/) (2019)
- [Phishing in Organizations: Findings from a Large-Scale and Long-Term Study](https://mlsanity.com/paper/2112.07498/) (2021)
- [PhishLang: A Real-Time, Fully Client-Side Phishing Detection Framework Using MobileBERT](https://mlsanity.com/paper/2408.05667/) (2024)
- [SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation](https://mlsanity.com/paper/2412.11109/) (2024)
- [That Ain't You: Detecting Spearphishing Emails Before They Are Sent](https://mlsanity.com/paper/1410.6629/) (2014)
- [Weak Links in Authentication Chains: A Large-scale Analysis of Email Sender Spoofing Attacks](https://mlsanity.com/paper/2011.08420/) (2020)
- [URL Inspection Tasks: Helping Users Detect Phishing Links in Emails](https://mlsanity.com/paper/2502.20234/) (2025)
- [VisualPhishNet: Zero-Day Phishing Website Detection by Visual Similarity](https://mlsanity.com/paper/1909.00300/) (2019)

## Built on by (most cited first, 1 of 1)

- [Assessing Spear-Phishing Website Generation in Large Language Model Coding Agents](https://mlsanity.com/paper/2602.13363/) (2026)

## Abstract (arXiv)

Phishing email is a critical step in the cybercrime kill chain due to the high reachability of victims' email accounts and the low cost of launching phishing campaigns. This ever-evolving nature of phishing emails makes traditional rule-based and feature-engineering-based phishing email detectors fight an uphill battle in the cat-and-mouse game of defense and attack. In this work, we show that, large language models (LLMs) can be effectively exploited to generate profile-grounded spear-phishing, compromising major paradigms of phishing email detectors. To defend against such LLM-based spear-phishing attacks, we propose PiMRef, the first reference-based solution to detect ever-evolving phishing emails using knowledge-based invariants, targeting the identity-impersonation attacks that characterize spear-phishing. Our rationale lies in the fact that convincing phishing emails often include ``disprovable claims'', which contradict certain real-world facts. Technically, given an email, PiMRef (i) discovers the claimed identity of the sender, (ii) verifies the sender's email domain against a dynamically expandable knowledge base, and (iii) infers call-to-action instructions that encourage next-step engagement. Compared to existing baselines such as D-Fence, HelpHed, and ChatSpamDetector, PimRef reduces the false-positive rate to 0.81% while maintaining a recall of 90.7%-93.1% on conventional phishing benchmarks such as Nazario and PhishPot. On SpearMail, our newly constructed benchmark of 14,672 LLM-generated spear-phishing emails targeting 681 public profiles, PimRef reaches a recall of 86.4% without incurring additional false positives. Our code is publicly available at https://github.com/code-philia/PhishEmail.