arXiv:2607. 19396v1 Announce Type: new Abstract: Document-based LLM systems often flatten a PDF before guardrails inspect it.
By Pukaphol Thienpreecha ("Volk")
Delphi Scanner is a static malware detection system for Windows PE files that balances efficiency and interpretability. It employs a convolutional neural network to model Windows API sequences and a rule‑based interpretation layer to map APIs to high‑level malicious capabilities. Tested on over 190,000 PE files, it achieves 95.35% accuracy with a 1.53 MB model, and demonstrates robustness against out‑of‑distribution samples and adversarial manipulations.
By Bijied Brahimi, Vincent Cohadon, Gabriel Glazman, Rayan Al Mohaize, Omran Berjawi, Rida Khatoun
The paper introduces a neural network model that identifies text content file types, especially source code, with higher accuracy and speed than existing tools. Experiments on open-source files show the model is more accurate on average, runs about four times faster than Magika, and is 28% smaller in size.
By Manu Nandan, Michael Brautbar, Edward Raff
arXiv:2606. 30586v1 Announce Type: cross Abstract: Most corporate workplace environments enforce policies and technical controls that limit the storage of sensitive data on client endpoints.
By Gervais Hatungimana, Abdun Naser Mahmood, Mohammad Jabed Morshed Chowdhury
arXiv:2606. 30819v1 Announce Type: cross Abstract: Generative AI has emerged as a significant cybersecurity threat, with several recent attack campaigns leveraging LLMs to generate code for malicious purposes via scripting languages such as PowerShell.
By Luciano Pianese, Vittorio Orbinato, Pietro Liguori, Roberto Natella
Most corporate workplace environments enforce policies and technical controls that limit the storage of sensitive data on client endpoints. Consequently, ransomware operators have evolved variants that expand their attack surface from local systems to network drives and shared storage resources.