Weak Links in LinkedIn: Enhancing Fake Profile Detection in the Age of LLMs
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866913955198271488 |
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| author | Gulati, Apoorva Kumar, Rajesh Agarwal, Vinti Sharma, Aditya |
| author_facet | Gulati, Apoorva Kumar, Rajesh Agarwal, Vinti Sharma, Aditya |
| contents | Large Language Models (LLMs) have made it easier to create realistic fake profiles on platforms like LinkedIn. This poses a significant risk for text-based fake profile detectors. In this study, we evaluate the robustness of existing detectors against LLM-generated profiles. While highly effective in detecting manually created fake profiles (False Accept Rate: 6-7%), the existing detectors fail to identify GPT-generated profiles (False Accept Rate: 42-52%). We propose GPT-assisted adversarial training as a countermeasure, restoring the False Accept Rate to between 1-7% without impacting the False Reject Rates (0.5-2%). Ablation studies revealed that detectors trained on combined numerical and textual embeddings exhibit the highest robustness, followed by those using numerical-only embeddings, and lastly those using textual-only embeddings. Complementary analysis on the ability of prompt-based GPT-4Turbo and human evaluators affirms the need for robust automated detectors such as the one proposed in this study. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_16860 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Weak Links in LinkedIn: Enhancing Fake Profile Detection in the Age of LLMs Gulati, Apoorva Kumar, Rajesh Agarwal, Vinti Sharma, Aditya Social and Information Networks Computer Vision and Pattern Recognition Computers and Society Large Language Models (LLMs) have made it easier to create realistic fake profiles on platforms like LinkedIn. This poses a significant risk for text-based fake profile detectors. In this study, we evaluate the robustness of existing detectors against LLM-generated profiles. While highly effective in detecting manually created fake profiles (False Accept Rate: 6-7%), the existing detectors fail to identify GPT-generated profiles (False Accept Rate: 42-52%). We propose GPT-assisted adversarial training as a countermeasure, restoring the False Accept Rate to between 1-7% without impacting the False Reject Rates (0.5-2%). Ablation studies revealed that detectors trained on combined numerical and textual embeddings exhibit the highest robustness, followed by those using numerical-only embeddings, and lastly those using textual-only embeddings. Complementary analysis on the ability of prompt-based GPT-4Turbo and human evaluators affirms the need for robust automated detectors such as the one proposed in this study. |
| title | Weak Links in LinkedIn: Enhancing Fake Profile Detection in the Age of LLMs |
| topic | Social and Information Networks Computer Vision and Pattern Recognition Computers and Society |
| url | https://arxiv.org/abs/2507.16860 |