hunt-llm-ai

To test chatbots for prompt injection vulnerabilities, use targeted payloads to probe for direct and indirect injection flaws. This process validates whether the target system is susceptible to system-prompt extraction or unauthorized instruction overrides.

Indirect prompt injection in RAG pipelines occurs when malicious instructions are embedded within retrieved data sources. Attackers exploit this to manipulate the LLM's behavior by injecting payloads into untrusted documents that the pipeline processes.

Verify OOB data exfiltration in LLM applications by triggering markdown image callbacks, tool-use execution, or DNS-only channels. Proving impact requires an OOB callback or verifiable data leak to confirm the vulnerability exists.

Yes, you can test autonomous agents for insecure tool-use primitives. This involves evaluating agentic security risks like tool parameter injection and memory poisoning to validate potential impact and verify unsafe execution paths.

Distinguish actual LLM security flaws from model confabulation by enforcing a False-Positive Gate. This required validation mechanism ensures reported vulnerabilities are verified through reproducible payloads rather than hallucinated model outputs.

The best way to pentest AI security flaws is to target chatbots, RAG pipelines, and autonomous agents with verifiable exploitation methods. This approach validates injection vectors and insecure tool usage while proving impact through data leaks.