Quick answer: Artificial intelligence and large language model (LLM) application penetration testing is a security service that uncovers the unique flaws of an organization's AI powered applications, chatbots and agent systems. The most common flaw of these applications is prompt injection: an attacker hijacks the instruction given to the model and steers the system outside its purpose. Other risks are sensitive data leakage, unauthorized tool calls and training data poisoning. DSET runs this test with its local AI engine KAOS and verifies every finding with a working proof.

AI applications spread fast, but their security tests often lagged behind. A language model application carries all the flaws of a classic web application and adds a unique new attack surface on top. AI application penetration testing is a service that examines this new surface through an attacker's eyes. This article explains what LLM penetration testing is, which flaws it targets and the DSET and KAOS approach.

What is AI application penetration testing

AI application penetration testing tests the flaws of an application, chatbot or agent system built on a language model through controlled attacks. The goal is to prove whether an attacker can hijack the instruction given to the model, whether the model reaches data it should not and whether the tools connected to the model can be abused. The frame of this test is the OWASP LLM Top 10 list; for detail the prompt injection and OWASP LLM Top 10 source is the reference point.

Which flaws it targets

The flaws of language model applications carry a logic different from classic software flaws: the attack often happens not in the code but in the language given to the model.

Flaw class What it means Typical result
Prompt injection Attacker hijacks the model's instruction System goes outside its purpose
Sensitive data exposure Model returns secret data in its answer Data leak
Unauthorized tool call A tool connected to the model is abused Unauthorized operation, privilege overreach
Excessive agency Agent runs with more privilege than needed Wide impact area
Indirect injection Malicious instruction comes from external content Hidden steering

The essence of the table is: in a language model application the attack surface covers both the classic software layer and the language given to the model itself. For the test of the classic layer the web application penetration testing methodology is complementary, and for the backend interfaces connected to the model our API penetration testing service.

Why agent systems are riskier

When a language model only produces text the risk is limited; but when tools are connected to the model, that is when the model can send email, run code or reach a database, a prompt injection turns directly into an action. For this reason the testing of agent systems focuses on two questions: which tools does the model reach and with what language can an attacker make it call these tools? To see that a fix actually works after a finding is verified, retest and remediation verification is needed.

The KAOS and DSET approach

DSET runs AI application penetration testing with its local AI engine KAOS. DSET has experienced both a defense side and an attack side in this field; because KAOS itself is an AI system and was hardened against prompt injection and abuse during its development. KAOS tests the instruction an application gives the model with different injection methods, tests sensitive data leakage and unauthorized tool calls and reports every flaw it finds with a working proof, with the principle of proof based verification. The difference is this: it does not settle for saying an attack is theoretically possible, it proves it with a real exploit.

Frequently asked questions

Is prompt injection really a serious risk? Yes, and it is as serious as how many tools are connected to the model. When the model only produces text the impact of an injection is limited. But when the model can send email, reach a database or trigger an operation, an injection turns directly into an action. For this reason the risk rises not with the model but with the size of the privilege given to the model.

I use a ready made model, is a test still needed? Yes. The risk is often not in the model itself but in the application that wraps the model and the privilege given to it. Even if you use a ready made model, what data you give that model, which tools you connect and how you pass user input are your responsibility. The real subject of the test is this application layer.

Does this test replace classic web penetration testing? No, it complements it. An AI application continues to carry all the flaws of a classic web application; it only adds a new layer on top. For this reason a complete test covers both the classic web and API layer and the new attack surface unique to the model together.

Sources

To examine your AI powered applications, chatbots and agent systems with a proof based test, contact DSET. We provide AI application penetration testing and consulting from our Ankara Hacettepe Teknokent laboratory.