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Put the model call behind your server, decide what data leaves your app, and treat both prompts and model responses as untrusted input. An LLM integration is an application boundary—not just an SDK call. Before launch, limit the model’s access to tools, validate consequential outputs in your own code, and instrument the feature so you can detect failures and behavior changes.
What does a safe LLM integration look like?
A typical request passes through several parts of your system: a user’s input, your application’s instructions, any retrieved documents or conversation history, a model provider, and possibly tools or saved state. Each handoff can expose data or create an opportunity for an unsafe action. Design the whole flow rather than treating the provider SDK as the security boundary.
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A practical starting sequence is to keep privileged credentials on the server, map the data flow, define what the model is allowed to do, validate anything that comes back, and test and monitor the feature before and after launch.
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How should the app make its first model request?
Make provider API calls from a trusted server-side component. A browser or mobile app is distributed to users, so a privileged API key embedded in its code can be extracted and reused. The server can authenticate the user, apply your own limits, select the allowed model operation, and keep the provider credential out of client code.
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OpenAI’s Developer quickstart illustrates one provider’s setup pattern: create an API key, expose it to the server process as an environment variable, install the SDK for the chosen runtime, and make a request. The exact SDK and request format depend on the provider and runtime; follow the current documentation for the API you use.
Keep the provider call behind an application route or service that checks the user’s identity and entitlement before sending a request. Do not let the client choose arbitrary provider credentials, unrestricted tools, or privileged system instructions. Set request and response size limits and a timeout appropriate to the feature, and return a controlled error if the provider is unavailable rather than exposing internal details.
What data will leave your app or be stored?
Before integrating the call, trace the data from collection to deletion. Include more than the visible prompt: retrieved material, system instructions, model responses, tool arguments and results, logs, analytics, and persisted conversation or workflow state can all contain sensitive information.
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- Identify which fields are necessary for the feature and omit or redact the rest.
- Decide which application components and staff roles can access prompts, responses, and logs.
- Set retention periods for your own databases, logs, analytics, and backups, and explain relevant handling to users.
- Check the provider’s current data-use and retention terms for the specific API features you plan to use.
Do not treat “not used for training” as equivalent to “not retained.” OpenAI’s current API data-controls documentation says API data is not used to train or improve its models unless the customer opts in. It also says default abuse-monitoring logs may contain prompts and responses and are retained for up to 30 days, subject to stated exceptions and controls. Separately, some features persist application state. OpenAI’s Zero Data Retention and Modified Abuse Monitoring controls require approval, and those controls do not mean every feature has no application-state storage. Check the current endpoint-specific conditions before making a privacy claim.
Those are OpenAI-specific terms, not a description of every provider or of your own application’s storage. Provider settings cannot account for copies your app or its other services retain. Privacy and legal obligations also depend on the data, jurisdiction, and use case; review them with the appropriate advisers rather than treating a model setting as a compliance determination.
How do you handle prompt injection and untrusted content?
Assume user input and retrieved documents may contain instructions designed to manipulate the model. A document can say to reveal hidden information or take an action; placing it in a prompt does not make it trustworthy. A system prompt can guide model behavior, but it is not an authorization mechanism.
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OWASP’s 2025 Top 10 for Large Language Model Applications includes prompt injection, sensitive information disclosure, insecure output handling, denial of service, insecure plugin design, excessive agency, and overreliance. These are risk categories, not proof that a particular app is vulnerable. They are useful prompts for threat-modeling the places where your app accepts content, retrieves data, invokes tools, and acts on model output.
- Keep authorization checks outside the prompt: your application should decide which records and actions the user may access.
- Limit retrieved context to material the authenticated user is permitted to see.
- Do not place secrets in model context unless the feature genuinely requires them and the data flow is acceptable.
- Test with adversarial instructions embedded in user messages and retrieved documents.
How should tools and model output be constrained?
Give each tool the minimum needed capability
A model with tools can do more than generate text, so tool access should be designed like any other privileged application interface. Expose only the narrow operations the feature needs. Enforce identity, ownership, permissions, business rules, and input validation in ordinary application code—not by asking the model to follow instructions.
For actions with meaningful consequences, add a human confirmation or a reversible intermediate step. For example, a proposed change can be shown for approval before your application commits it. The right safeguard depends on the impact of the action; there is no universal tool architecture that fits every app.
Validate responses before using them
Treat generated content as data, not authority. If a response will be used in SQL, a shell command, HTML, a database update, or a workflow, validate and encode it for that exact destination. Prefer fixed, allowlisted operations over executing free-form generated instructions.
A structured response format can make output easier to parse, but successfully parsing a response does not establish that it is true, safe, authorized, or within business rules. Check types, ranges, ownership, and required invariants in application code before taking action. OWASP identifies insecure output handling as a major LLM application risk.
How do you limit abuse, cost, and outages?
Model calls can consume significant resources, and OWASP’s denial-of-service risk discussion includes resource-heavy operations that can disrupt a service or increase costs. Put controls around both the request path and the amount of work each request can trigger.
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- Limit input and output size, and cap the number of model or tool calls a request can make.
- Apply per-user or per-account rate limits and request budgets appropriate to your traffic and abuse risks.
- Use timeouts and a graceful fallback or error path; do not leave user requests waiting indefinitely.
- Decide how retries work so temporary failures do not create uncontrolled repeated calls.
Choose limits from your application’s threat and traffic model. A control that is too loose can invite abuse; one that is too tight can block legitimate use. Monitor the effect and adjust deliberately.
What should you test and monitor before and after launch?
Build evaluation cases around the feature’s expected use, edge cases, and abuse attempts. Include examples where the model should refuse, where retrieved content contains hostile instructions, and where a plausible-looking response violates a business rule. Test the complete application path—including authorization and tool execution—not just whether the model’s prose sounds right.
OpenAI’s API reference recommends logging request IDs in production and documents request and token rate-limit headers. It also notes that prompting behavior can change between model snapshots, and recommends pinned model versions and evals for more consistent behavior. These are OpenAI-specific recommendations; use the current reference for the API and model version you deploy.
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- Track latency, errors, refusals, rate-limit events, cost, and feature-specific quality signals.
- Run your evaluation cases when prompts, tools, application logic, or model versions change.
- Have a rollback or disable path for the feature if quality or safety degrades.
Monitoring tells you what happened in production; evaluations help you detect changes in known cases. Neither guarantees correct behavior, so keep application-side permission checks and validation in place.
Where does moderation fit?
OpenAI’s Moderations API reference describes an endpoint that classifies text and/or image inputs for potentially harmful content. A moderation check can support a policy-specific safety pipeline, but it does not determine whether a user is authorized, prevent prompt injection, or make a downstream tool call safe. Use it as one control within the broader design, not as a substitute for access control or output validation.
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