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.

Detecting Attacks against Azure DevOps

This article explores detection opportunities for attacks against Azure DevOps, focusing on telemetry sources and logging limitations. It details how malicious actors can exploit Azure AD applications, steal Personal Access Tokens (PAT), and compromise DevOps pipelines. The research emphasizes the importance of multi-source logging and contextual analysis to detect sophisticated DevOps security incidents.

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Domain-specific prompt injection detection

Detecting Attacks against Azure DevOps