ai-prompt-injection-audit

To audit an LLM application for prompt injection vulnerabilities, scan your codebase to identify insecure prompt construction, unvalidated tool arguments, and excessive agency in AI agents.

Securing RAG pipelines against indirect prompt injection requires treating retrieved content as untrusted data, implementing role-based message separation, and enforcing strict validation on tool calls.

Insecure output handling in AI agents allows manipulated LLM responses to trigger unauthorized privileged actions, making strict output sanitization and least-privilege tool scoping necessary to prevent data exfiltration.

You can use this audit framework to implement defense-in-depth for LLM agents by applying actionable strategies like role separation, output encoding, and human-in-the-loop approval gates.

You need role-based message separation in LLM security controls when reviewing AI agents to ensure system prompts remain isolated from untrusted user input and prevent system prompt leakage.

Your AI agent performs unauthorized privileged actions during tool execution due to excessive agency and unvalidated tool arguments, requiring least-privilege tool scoping and strict validation to remediate.