Building with LLMs? The Attack Surface Just Got a Lot Bigger.

If your app takes user input and passes it into a prompt, it has a prompt injection surface. We test whether malicious users can override your system prompt, extract your instructions, make the model do things it shouldn't, or use your AI feature to attack other users.

RAG systems that pull from a shared knowledge base, conversation memory that persists incorrectly across sessions, and LLM context windows that accumulate prior user data are all paths to cross-user data leakage. We trace every data path in your AI layer.

Without rate limiting, max token controls, and spend caps at the user level, a single malicious request can cost you thousands of dollars. We audit every LLM call for missing guards, and test whether adversarial inputs can force expensive completions.

AI agents with tool calling — file access, web browsing, code execution, API calls — need sandboxing and scope limits that AI-generated agent code almost never implements correctly. We test whether your agent can be coerced into taking destructive actions.

Retrieval-augmented generation systems store sensitive data in vector databases with embeddings. Access control, namespace isolation, and injection of malicious documents into the retrieval index are all risks we audit specifically.

If your app acts on LLM output — parsing structured data, executing suggestions, routing decisions — weak output validation is a reliability and security risk. We check every place your code trusts LLM output without sufficient validation.