Papers by Joshua Garland
How Reliable Are AI-Generated-Text Detectors? An Assessment Framework Using Evasive Soft Prompts (2023.findings-emnlp)
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| Challenge: | Existing methods to detect AI-generated text are inadequate, causing misuse of the text. |
| Approach: | They propose a universal evasive prompt framework that can prompt any PLM to generate “human-like” text that can mislead detectors. |
| Outcome: | The proposed approach can prompt any PLM to generate “human-like” text that can mislead detectors. |
Defending Against Social Engineering Attacks in the Age of LLMs (2024.emnlp-main)
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Lin Ai, Tharindu Kumarage, Amrita Bhattacharjee, Zizhou Liu, Zheng Hui, Michael Davinroy, James Cook, Laura Cassani, Kirill Trapeznikov, Matthias Kirchner, Arslan Basharat, Anthony Hoogs, Joshua Garland, Huan Liu, Julia Hirschberg
| Challenge: | Existing research has developed frameworks to understand human-to-human CSE attacks. |
| Approach: | They propose a modular defense pipeline that improves detection at both the message and conversation levels. |
| Outcome: | The proposed model can be exploited to facilitate chat-based social engineering attacks and generate high-quality CSE content, but their detection capabilities are suboptimal, leading to increased operational costs for defense. |