prompt-injection

To prevent prompt injection attacks in LLM agents, you must audit application surfaces to identify vulnerable interpolation patterns, validate prompt construction, and enforce strict permission boundaries for tool calling.

Indirect prompt injection in RAG pipelines occurs when untrusted external data chunks manipulate the LLM. Security audits must trace these indirect inputs to prevent privilege escalation and unauthorized agent actions.

Auditing MCP security and tool calling permission boundaries involves evaluating function validation, gating destructive actions, and testing for confused-deputy scenarios under real user-role contexts to prevent authorization bypass.

Testing AI agent authorization bypass and privilege escalation requires mapping AI attack surfaces, assessing agent loop risks, and verifying data isolation across web apps and API surfaces under various user-role contexts.

Securing system prompts and preventing data leaking requires auditing prompt construction for missing instruction boundaries, secret exposure risks, and insecure output handling like unsafe HTML rendering or code execution paths.

Yes, this prompt injection audit applies to web apps and API surfaces, evaluating direct, indirect, and cross-privilege injection scenarios across RAG pipelines, tool calling, and MCP-based agent architectures.