prompt-injection

To test for prompt injection vulnerabilities, analyze your language model application setup instructions to map attack surfaces across user inputs and external content sources, identifying risks of malicious prompt manipulation and system prompt leakage.

Securing a RAG pipeline against indirect prompt injection involves assessing external data sources for malicious content, mapping potential attack vectors, and integrating architectural defenses to prevent data exfiltration and privilege escalation.

Prompt injection risk analysis prevents system prompt leakage by comprehensively mapping input channels and external sources, testing the chatbot's architectural defenses, and recommending mitigations against malicious prompt manipulation.

Yes, you can assess external data sources for LLM security risks by analyzing how external content interacts with your language model, ensuring applications prevent malicious manipulation, exfiltration, and privilege escalation through comprehensive mapping and testing.

Architectural defenses preventing privilege escalation in language model workflows include integrating security best practices into LLM workflows, mapping input channels, and applying recommendations to block malicious prompt manipulation and unauthorized access.