LLM APPLICATION PENETRATION TESTING
LLM application penetration testing: check your AI assistant cannot be hijacked
Testing of LLM-based applications to the OWASP Top 10 for LLM. Prompt injection, data disclosure, excessive agent permissions and the security of tool integrations.
WHY IT MATTERS
The model does not tell instructions from data, and you gave it access
LLM-based applications connect the model to your data, tools and actions. The problem is that the model treats the content it processes as a potential instruction. Text from a document, an email or a web page can take over the assistant.
One case from our tests: in a RAG application, a user-uploaded document contained a hidden instruction telling the assistant to reveal another user’s context. That is indirect prompt injection, a class of attack a traditional app test does not cover at all.
WHAT WE CHECK
The full OWASP Top 10 for LLM
We tailor scope to the architecture: plain chat, RAG or an agent with tool access.
OUR APPROACH
We attack not just the model, but the whole application around it
LLM application security is not just the model itself. What matters is the data and tools it can access, how input is filtered and what the app does with the response. We test that whole chain, not just individual prompts.
We combine LLM-specific techniques with a classic test of the app and API around the model. It is exactly at the seam between the model and the rest of the system that the worst vulnerabilities arise, for example an agent that performs a dangerous action on an instruction hidden in data.
COMPLIANCE
AI security is entering regulation
AI deployments fall under growing security and risk-management requirements, including the AI Act.
STANDARDS & CERTIFICATIONS
We work to recognized methodologies, not gut feeling
Every project is run by certified pentesters and based on public standards. That makes the result repeatable, auditable and comparable across vendors.
We share the full list of certifications and standards on request, together with a sample test scope.
HOW WE DO IT
A repeatable process for AI applications
EVIDENCE
Numbers behind every promise
Every test is run by certified pentesters, and we document the result with reproduction steps, evidence and a verified remediation path. Proof, not a promise.
KNOWLEDGE
LLM application security testing in practice
Why apps built on a language model need a new approach
An application built on a language model takes user text as an instruction, so the line between data and command blurs. We test it to the OWASP Top 10 for LLMs, covering both the model itself and its integrations with data and tools.
A classic scanner is not enough here, because the risk does not sit in a single request but in how the model interprets content. So we work through scenarios in which an attacker tries to take control of the application behavior.
Prompt injection and data leakage
The most important class of risk is prompt injection: direct, where the user openly manipulates the instruction, and indirect, where a malicious instruction hides in the data the model retrieves. We check whether this can bypass rules or extract the hidden system prompt.
We also test data leakage: whether the model exposes content the user should not access, including other users data or knowledge base information. This is a common result of over-trusting whatever the model receives in its context.
Excessive agency in agents and integrations
When the model can call tools, send queries or trigger actions, the risk of excessive agency appears. We check whether the application can be forced into an operation the user should not be able to perform, and whether the model output is handled safely.
Unsafe handling of the model response, for example executing it as code or a query, can turn a vulnerability into a full takeover. That is why we look not only at the model but at the entire chain around it.
What you get and when to test
The report describes every attack scenario with evidence and a concrete business impact, along with recommendations covering the system prompt, validation, least privilege and data filtering. We translate the risk into the language of decisions, not just technique.
An LLM application is worth testing before the feature reaches users and after every change to the model, system prompt or integrations. Each of these can open a new path to abuse.
FAQ
Common questions
Do you test the model or the application?
Do you cover agents and RAG?
Does this replace an API test?
Is the retest included?
RELATED
Related reading
CASE STUDIES
Case studies in this area
REFERENCES
“The project was delivered professionally and on time, with a strong grasp of both technology and business. We were impressed by their cybersecurity expertise and partnership approach.”
