Defending Against Social Engineering Attacks in the Age of LLMs
<|reference_start|>Defending Against Social Engineering Attacks in the Age of LLMs: The proliferation of Large Language Models (LLMs) poses challenges in detecting and mitigating digital deception, as these models can emulate human conversational patterns and facilitate chat-based social engineering (CSE) attacks. This study investigates the dual capabilities of LLMs as both facilitators and defenders against CSE threats. We develop a novel dataset, SEConvo, simulating CSE scenarios in academic and recruitment contexts, and designed to examine how LLMs can be exploited in these situations. Our findings reveal that, while off-the-shelf LLMs generate high-quality CSE content, their detection capabilities are suboptimal, leading to increased operational costs for defense. In response, we propose ConvoSentinel, a modular defense pipeline that improves detection at both the message and the conversation levels, offering enhanced adaptability and cost-effectiveness. The retrieval-augmented module in ConvoSentinel identifies malicious intent by comparing messages to a database of similar conversations, enhancing CSE detection at all stages. Our study highlights the need for advanced strategies to leverage LLMs in cybersecurity.<|reference_end|>
@article{ai2024defending,
    title={Defending Against Social Engineering Attacks in the Age of LLMs},
    author={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},
    journal={arXiv preprint arXiv:2406.12263},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.12263},
    primaryClass={cs.CL}
}