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A domain-specific machine learning approach was developed to detect prompt injection attacks in job application contexts using a fine-tuned DistilBERT classifier. The model was trained on a custom dataset of job applications and prompt injection examples, achieving approximately 80% accuracy in identifying potential injection attempts. The research highlights the challenges of detecting prompt injection in large language models and emphasizes that such detection methods are just one part of a comprehensive security strategy.
This article explores the security risks of granting Large Language Models (LLMs) control over web browsers. Two attack scenarios demonstrate how prompt injection vulnerabilities can be exploited to hijack browser agents and perform malicious actions. The article highlights critical security challenges in LLM-driven browser automation and proposes potential defense strategies.