Every enterprise deploying AI-powered features — customer-facing chatbots, internal copilots, automated document processing, AI-assisted decision making — is introducing an entirely new class of attack surface that traditional security testing was never designed to evaluate.
At Simuna Infosec, we've been testing AI and LLM applications for enterprises since these systems moved from research labs into production. Here's what we've learned about how these systems break — and how to test them before attackers do.
Why AI Applications Are Different
Traditional applications follow deterministic logic: input A produces output B. LLM-powered applications are probabilistic: the same input can produce different outputs depending on context, system prompts, conversation history, and model state. This fundamental difference means that traditional VAPT techniques — while still necessary for the application layer — are insufficient for the AI-specific attack surface.
The OWASP Top 10 for LLMs — And Beyond
The OWASP Top 10 for Large Language Model Applications provides a useful framework. We test against all ten categories, but our experience has shown that the most impactful real-world attacks often combine multiple categories or exploit gaps between them.
Prompt Injection (Direct & Indirect) Direct prompt injection attempts to override the system prompt through user input. More dangerous is indirect prompt injection, where malicious instructions are embedded in data the LLM processes — emails, documents, web pages, database records. We test both attack paths across every data source the LLM touches.
Data Leakage & Training Data Extraction Can an attacker extract sensitive information from the model's training data? Can they manipulate conversations to reveal system prompts, API keys, or internal instructions? We systematically probe for memorization leakage and context window exploitation.
Excessive Agency & Tool Abuse Modern LLM applications often have access to tools — database queries, API calls, file operations, email sending. We test whether an attacker can manipulate the model into executing unintended actions through its tool-use capabilities.
Our AI/LLM Testing Methodology
We apply the same rigor to AI testing as we do to traditional VAPT — our 16-step methodology adapted for AI-specific attack vectors. This includes testing the application layer (authentication, authorization, API security) AND the AI-specific layer (prompt attacks, guardrail bypass, data leakage, agency abuse).
What Enterprises Should Do Now
If you've deployed or are deploying AI-powered features: test them before attackers do. The window between "AI feature launches" and "AI feature gets exploited" is shrinking rapidly. Expert-led testing that combines traditional application security with AI-specific attack techniques is the only way to understand your real risk.