llm-prompt-injection

To test AI applications for prompt injection, use a structured testing methodology covering direct instruction override, role play, RAG poisoning, and web browsing injection to manipulate AI behavior and identify vulnerabilities.

Indirect prompt injection in LLM security involves embedding malicious instructions within external data sources, which manipulates the AI's behavior when processed, leading to potential data exfiltration and unauthorized tool execution.

To identify data exfiltration risks via tool calling abuse in LLMs, simulate unauthorized actions and exploit function calling vulnerabilities to uncover security gaps in the application's tool access controls.

Common defense bypass techniques for LLM security testing include utilizing encoding, payload splitting, and few-shot manipulation to circumvent existing AI safety filters and successfully execute injection attacks.

Yes, performing LLM injection testing requires existing knowledge of AI/LLM security testing to effectively assess vulnerabilities like Model Context Protocol risks and classify the severity of discovered impacts.

The best way to assess the severity of LLM injection vulnerabilities is to apply a structured impact classification methodology after successfully executing defense bypass patterns and verifying data exfiltration techniques.