The paper introduces a prompt‑injection detection framework for email assistants that models attacks as a chain of stages. It combines a text detector, stage‑specific verifiers, rule‑based risk signals, user intent consistency checks, and a logistic decision policy. Experiments on five benchmarks show the framework outperforms pretrained detectors, achieving a mean F1 of 0.406 versus 0.216, and demonstrate that training on benign emails resembling attacks reduces false alarms.
By Ahmad Hashmi, Dhyey Patel, Yunting Yin
arXiv:2606. 11471v1 Announce Type: cross Abstract: The expansion of the digital domain has resulted in a substantial increase in digital communication, with email emerging as one of the most prominent channels.
By Warren Fernando, Nikos Komninos
arXiv:2507.15393v2 Announce Type: replace-cross
Abstract: 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 la...
By Ruofan Liu, Yun Lin, Yuxin Wang, Xiwen Teoh, Zhenkai Liang, Gongshen Liu, Haojin Zhu, Jin Song Dong
arXiv:2606. 21690v2 Announce Type: replace-cross Abstract: Phishing is a multi-modal threat.
By Saifelden M. Ismail, Aser O. Ibrahim, Omar A. Mahmoud
arXiv:2602. 09222v2 Announce Type: replace-cross Abstract: Large language model (LLM) based web agents are increasingly deployed to automate complex online tasks by directly interacting with web sites and performing actions on users' behalf.
By Georgios Syros, Evan Rose, Brian Grinstead, Christoph Kerschbaumer, William Robertson, Cristina Nita-Rotaru, Alina Oprea
arXiv:2608. 15893v1 Announce Type: new Abstract: The rise of social media bots poses a persistent threat, enabling misinformation, opinion manipulation, and the erosion of trust in online platforms.
By Nof Orenstein, Yoni Birman
arXiv:2607. 18429v1 Announce Type: cross Abstract: Phishing emails remain one of the most persistent cybersecurity threats, and machine-learning classifiers are widely used to detect them.
By Tanveer Ahmed, Seyedali Pourmoafil
arXiv:2511. 12085v3 Announce Type: replace-cross Abstract: Phishing and related cyber threats are becoming increasingly sophisticated, with email-based phishing remaining the most persistent attack vector.
By Sajad U P
AURA: Adaptive Uncertainty-Routed Analysis for Email Threat Detection is a multimodal system that evaluates both email content and embedded URLs to detect spam and phishing. It uses a two-layer approach: first, a URL classifier estimates prediction uncertainty, and only messages with high uncertainty are passed to a fine-tuned transformer encoder for deeper semantic analysis. Evaluated on eight diverse training corpora and two real-world datasets covering a decade of attacks, AURA achieves a macro F1-score of 0.9858 in-distribution and maintains scores above 0.94 on the NazPhish-Eval and GuenterTrap-Eval datasets, demonstrating strong generalization to new attack scenarios.
By Omran Berjawi, Walid fahs, Rida Khatoun
arXiv:2607. 06963v1 Announce Type: cross Abstract: Large Language Models (LLMs) and generative AI (GenAI) systems, such as ChatGPT, Claude, Gemini, LLaMA, Copilot, Stable Diffusion by OpenAI, Anthropic, Google, Meta, Microsoft, Stability AI, respectively, are revolutionizing cybersecurity, enabling both automated defense and sophisticated attacks.
By Kiarash Ahi, Saeed Valizadeh
The paper investigates prompt injection attacks on 14 open‑source and 3 closed‑source large language models (LLMs), introducing a new metric called Attack Success Probability (ASP) that accounts for uncertainty in model responses. It demonstrates that a simple hypnotism attack can trigger objectionable behavior in models such as StableLM2, Mistral, Openchat, and Vicuna, achieving roughly 90% ASP. The study highlights that moderately well‑known LLMs are particularly vulnerable, underscoring the importance of public awareness and effective mitigation strategies.
By Jiawen Wang, Pritha Gupta, Eyke H\"ullermeier, Xiaoxue Gao, Nancy F. Chen
arXiv:2607. 10402v1 Announce Type: cross Abstract: Large language models (LLMs) have transformed misinformation from a primarily content-centric problem into a broader ecosystem-level security challenge.
By Lingwei Wei, Dou Hu, Wei Zhou, Songlin Hu, Philip S. Yu