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You connected a Spring Boot service to an LLM. You added a SystemMessage with business logic and shipped it. Now you face two distinct security risks.
Most teams only worry about one. You must understand both.
Prompt Injection The attacker tries to override your instructions. They embed new directives in the user input to change how the model behaves.
System Prompt Leakage The attacker tries to read your hidden instructions. They craft messages to make the model repeat back its private system prompt.
The difference is the goal. One seeks control. The other seeks information.
Why this happens Models process the entire context as one flat sequence. There is no cryptographic boundary between your system instructions and user input. If you concatenate them into one string, you create a massive vulnerability.
How to fix it
Structural Separation Do not build one long string. Use the ChatClient API to place instructions in a SystemMessage turn and user content in a UserMessage turn. This creates a logical boundary.
Never Store Secrets in Prompts Do not put passwords or API keys in the system prompt. These show up in logs, traces, and dashboards. Use a secrets manager and inject values at runtime.
Implement Output Guards Use canary strings. If the model response contains fragments of your system instructions, block the response.
Validate Everything Use Bean Validation on your request DTOs. Use @Pattern to reject common injection phrases. Validate model outputs against a strict schema before using them in downstream code.
Use Advisors Spring AI Advisors allow you to intercept prompts and responses. This is the best place to enforce security rules without cluttering your business logic.
Security is layers. A deny-list is a start, but it is not a complete shield. Separate your turns, validate your boundaries, and never trust model output.
Source: https://dev.to/securitystefan/system-prompt-leakage-vs-prompt-injection-in-spring-boot-ai-56eh
Optional learning community: https://t.me/GyaanSetuAi