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Study Assesses LLMs in Personal Information Extraction and Mitigation

/ 1 min read

🛡️✨ LLMs enhance personal information extraction but can be countered with prompt injection. A recent study evaluates the effectiveness of large language models (LLMs) in extracting personal information, such as names and contact details, from publicly available profiles. Traditional extraction methods have proven inadequate, while LLMs demonstrate superior accuracy. The research involved benchmarking ten LLMs against five datasets, including a synthetic dataset generated by GPT-4. Notably, the study introduces a novel mitigation strategy using prompt injection, which significantly reduces the effectiveness of LLM-based attacks, reverting them to less effective traditional methods. These findings highlight both the potential risks posed by LLMs in security contexts and the importance of developing effective countermeasures.

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