10 papers
The Phish, The Spam, and The Valid: Generating Feature-Rich Emails for Benchmarking LLMs
Rebeka Toth, Tamas Bisztray, Nils Gruschka
In this paper, we introduce a metadata-enriched generation framework (PhishFuzzer) that seeds real emails into Large Language Models (LLMs) to produce 23,100 diverse, structurally…
The Hidden DNA of LLM-Generated JavaScript: Structural Patterns Enable High-Accuracy Authorship Attribution
Norbert Tihanyi, Bilel Cherif, Richard A. Dubniczky +2
In this paper, we present the first large-scale study exploring whether JavaScript code generated by Large Language Models (LLMs) can reveal which model produced it, enabling relia…
I Know Which LLM Wrote Your Code Last Summer: LLM generated Code Stylometry for Authorship Attribution
Tamas Bisztray, Bilel Cherif, Richard A. Dubniczky +6
Detecting AI-generated code, deepfakes, and other synthetic content is an emerging research challenge. As code generated by Large Language Models (LLMs) becomes more common, identi…
DFIR-Metric: A Benchmark Dataset for Evaluating Large Language Models in Digital Forensics and Incident Response
Bilel Cherif, Tamas Bisztray, Richard A. Dubniczky +3
Digital Forensics and Incident Response (DFIR) involves analyzing digital evidence to support legal investigations. Large Language Models (LLMs) offer new opportunities in DFIR tas…
CASTLE: Benchmarking Dataset for Static Code Analyzers and LLMs towards CWE Detection
Richard A. Dubniczky, Krisztofer Zoltán Horvát, Tamás Bisztray +3
Identifying vulnerabilities in source code is crucial, especially in critical software components. Existing methods such as static analysis, dynamic analysis, formal verification,…
Vulnerability Detection: From Formal Verification to Large Language Models and Hybrid Approaches: A Comprehensive Overview
Norbert Tihanyi, Tamas Bisztray, Mohamed Amine Ferrag +4
Software testing and verification are critical for ensuring the reliability and security of modern software systems. Traditionally, formal verification techniques, such as model ch…