OWASP Top 10 for LLMs: How to Test the Security of Generative AI Applications

Why LLM Applications Need a Different Approach

Traditional security testing focuses on vulnerabilities such as SQL Injection, XSS, and authentication flaws — all related to how code processes structured input.

LLMs process unstructured natural language as instructions, which creates a fundamentally different class of risk: under certain conditions, the user's own input can be interpreted by the model as a command — even when the developer intended it to be treated purely as content.

Key Risks in the OWASP Top 10 for LLMs

1. Prompt Injection

The best-known and most widely discussed risk. An attacker manipulates the input given to the model to bypass system instructions, extract restricted information, or induce the model to perform unauthorized actions.

There are two main variants: direct, when the user inserts the malicious prompt themselves, and indirect, when the malicious instruction is embedded in external content the model processes — such as a document, email, or web page the LLM is asked to summarize or analyze.

2. Sensitive Data Leakage

Models connected to knowledge bases, internal documents, or corporate APIs can expose information the user should not have permission to access — especially when access control is not enforced consistently between the system that owns the data and the AI layer.

3. Model Supply Chain

Risks related to the components used to build the solution: third-party models, plugins, frameworks, and training data — each introducing an additional trust surface that needs to be evaluated.

4. Training Data Poisoning

When a model is fine-tuned with attacker-controlled data, it is possible to introduce biases, backdoors, or unwanted behaviors that only surface under specific conditions.

5. Improper Handling of Model Output

When the model's response is consumed directly by other systems without validation — for example, executed as code or inserted straight into a web page — traditional vulnerabilities such as XSS or remote code execution can be reintroduced through the LLM's output.

6. Excessive Permissions (Excessive Agency)

Models connected to tools and APIs that can perform actions (send emails, modify records, execute transactions) need strictly limited permissions. A manipulated model with broad permissions can cause real damage, not just expose information.

7. System Prompt Leakage

System instructions often contain business logic, internal rules, or information the company does not intend to make public. Prompt extraction techniques aim to expose those instructions.

8. Overreliance on Model Responses

Models can generate incorrect answers in a convincing way (hallucinations). When those answers feed automated decisions without human validation, the business impact can be significant.

How a Pentest for AI Applications Works

A pentest for LLM-based applications combines traditional application security techniques with tests specific to the model:

• Structured attempts at direct and indirect Prompt Injection
• Tests for extracting the system prompt and training data
• Assessment of access control in knowledge base (RAG) integrations
• Analysis of the permissions granted to the model in integrations with APIs and external tools
• Security testing of the APIs and the infrastructure hosting the application
• Assessment of how model outputs are handled before feeding other systems

Conclusion

Generative AI applications do not replace the need for traditional application security — they add an entirely new layer of risk on top of it.

Companies launching chatbots, internal assistants, or any LLM-based solution connected to corporate data and systems need to treat this assessment as part of the process — not as an optional step to be done after the product is already in production.