Is the Digital Forensics and Incident Response Pipeline Ready for Text-Based Threats in LLM Era?

TL;DR

This paper evaluates the readiness of the DFIR pipeline for detecting and attributing NTG-authored texts in the AI era, revealing significant vulnerabilities and proposing advanced strategies for improvement.

Contribution

It introduces a novel human-NTG co-authorship attack and provides a comprehensive evaluation of DFIR vulnerabilities using diverse datasets and models.

Findings

Traditional DFIR methods struggle with NTG attribution.

Model sophistication and stylistic similarities reduce attribution accuracy.

Adversarial learning and stylization can improve NTG source detection.

Abstract

In the era of generative AI, the widespread adoption of Neural Text Generators (NTGs) presents new cybersecurity challenges, particularly within the realms of Digital Forensics and Incident Response (DFIR). These challenges primarily involve the detection and attribution of sources behind advanced attacks like spearphishing and disinformation campaigns. As NTGs evolve, the task of distinguishing between human and NTG-authored texts becomes critically complex. This paper rigorously evaluates the DFIR pipeline tailored for text-based security systems, specifically focusing on the challenges of detecting and attributing authorship of NTG-authored texts. By introducing a novel human-NTG co-authorship text attack, termed CS-ACT, our study uncovers significant vulnerabilities in traditional DFIR methodologies, highlighting discrepancies between ideal scenarios and real-world conditions. Utilizing 14 diverse datasets and 43 unique NTGs, up to the latest GPT-4, our research identifies substantial vulnerabilities in the forensic profiling phase, particularly in attributing authorship to NTGs. Our comprehensive evaluation points to factors such as model sophistication and the lack of distinctive style within NTGs as significant contributors for these vulnerabilities. Our findings underscore the necessity for more sophisticated and adaptable strategies, such as incorporating adversarial learning, stylizing NTGs, and implementing hierarchical attribution through the mapping of NTG lineages to enhance source attribution. This sets the stage for future research and the development of more resilient text-based security systems.