prompt-injection

You audit AI-integrated applications for prompt injection vulnerabilities by identifying entry points, analyzing direct and indirect injection vectors, and following a step-by-step audit workflow to secure LLM applications.

Common prompt injection vectors in LLM applications include direct user inputs, indirect injection through external data sources, and tool-description injection where malicious commands are embedded in tool metadata.

You assess the severity of LLM security vulnerabilities by calculating CVSS scoring for different vulnerability types and prioritizing exploitation goals based on their potential impact on data exfiltration and unauthorized access.

Yes, testing prompt injection vulnerabilities requires understanding of AI architecture and application security practices to effectively identify injection vectors and mitigate potential exploitation chains.

You can test for prompt injection vulnerabilities using proof-of-concept payloads provided by the skill to validate discovered injection vectors and demonstrate potential data exfiltration or unauthorized access scenarios.

AI applications vulnerable to prompt injection attacks include chatbots, IDE copilots, and email assistants that integrate LLMs and process external inputs through various application entry points.