The use of Large Language Models (LLM) is changing the structure of modern applications. Where clearly defined processes and fixed decision-making logic used to dominate, systems are now emerging that react flexibly and generate content depending on the context. This strength also leads to a new form of attack surface.
Many companies initially underestimate how comprehensively an LLM is integrated into processes. It processes texts, controls workflows, accesses external systems via plugins or tools and connects data sources that were previously separate. This creates risks that traditional security tests only partially cover.
How LLMs become vulnerable
A language model can be manipulated or misdirected. This ranges from minor disruptions to business-critical consequences. Prompt injection, uncontrolled execution of tools or the outflow of sensitive content are typical examples. It becomes particularly critical when an LLM prepares automated decisions or intervenes directly in processes.
Further risks arise from the RAG architecture: when models reload information from databases or documents, the quality and integrity of these sources play an overriding role. Incorrect content can falsify answers or influence decisions.
Why classic tests are not enough here
Web applications follow clear rules. An LLM, on the other hand, is based on probabilities and context. This means that identical inputs lead to different results depending on the system prompt, guardrails, data situation or plug-in behavior. A test must take these differences into account and proceed creatively.
This is why a pure tool test is not enough. Even standardized checklists only cover a small part of the possible points of attack. The decisive factor is a test strategy that understands the specific system and focuses precisely on the weak points.
SRC’s approach to LLM penetration tests
SRC combines methodical testing with an analysis of the actual architecture. The test therefore does not start with attacks, but with a structured understanding of the application.
Phase 1: Business Understanding
What role does the model play? What decisions does it influence. What data is processed. Which parts of the system are critical.
Phase 2: Threat modeling
In this step, we determine which threats are realistic.
Phase 3. test execution
The tests include attacks on prompts, tools, RAG paths, APIs and the processing of the output.
Phase 4: Reporting and measures
The result is a report containing findings, effects and recommendations for action.
What qualifies SRC for these tests
SRC has many years of experience in security-critical environments. This includes tests in payment transactions, in accordance with BSI specifications and in the Common Criteria environment. This experience ensures that tests remain reproducible and auditable.
Why companies should act
With the growing use of generative AI, responsibility is also increasing. Many systems access confidential data or control operational steps. A wrong decision can cause real damage.
Conclusion
LLM penetration tests are a necessary building block in the secure operation of modern AI systems. They form the basis for comprehensibly identifying risks and deriving measures.
