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Running an AI model on your own computer can reduce the prompts you send to a remote inference provider, but it does not automatically secure your files, model downloads, local interface, or connected tools. Protecting a local AI setup means limiting what it can reach, checking what you install, and keeping a person in control of sensitive actions.
These practices apply to open-weight models and other models executed locally. The details depend on the runtime and version you use: check its current official documentation for network exposure, authentication, file access, and data retention.
What does running AI locally protect—and what does it not?
“Local” describes where inference runs. It may mean prompts are not sent to a remote inference provider, but it does not establish how a particular application stores conversations, handles temporary files, or exposes an interface on your network. Nor does it make a downloaded model trustworthy or prevent an agent from misusing tools you have enabled.
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1. Check a model’s origin before importing it
Model weights and adapters are third-party software artifacts. Before loading one, consider where it came from, whether the publisher and provenance are clear, and whether the publisher provides integrity information you can check. A familiar download location is not, by itself, proof that a file is safe.
- Prefer files with a clearly identified publisher and a traceable source.
- When integrity information is provided, verify the downloaded file against it using a method supported by your operating system or runtime.
- Be especially cautious with files from unclear sources or with conversion steps you do not understand. Do not use your everyday environment for untrusted experiments.
OWASP’s Secure AI/ML Model Ops guidance identifies unvalidated third-party models and malicious model files as risks, and recommends validating models and protecting artifacts.
2. Keep the inference interface off the network unless you need access
A local server or interface may be reachable only from the computer, or it may listen on a network interface that other devices can reach. The actual behavior depends on the software and version. Check the runtime’s current official documentation and settings to find the listening address, network interfaces, and any authentication controls; do not assume defaults.
- If you use the model only on the computer running it, avoid exposing the service to the local network or internet.
- If another device needs access, use the runtime’s supported authentication and authorization controls, and allow access only to the people or devices that need it.
- Do not treat an obscure address or a private network as a substitute for authentication.
OWASP identifies inference APIs without authentication as a security issue and recommends authentication, authorization, input validation, and rate limiting.
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3. Give the model the smallest practical permissions
A text-generation task usually does not require unrestricted access to every personal file, shell command, device, or network destination. When configuring a runtime, retrieval system, or agent, grant access only to the resources needed for that task.
- Limit file access to a specific working folder rather than an entire home directory.
- Use read-only access when the model needs to inspect material but not change it.
- Enable only the tools the task requires, and restrict their actions and destinations where the software allows it.
Keep permission enforcement in the operating system or application configuration—not just in instructions written to the model. OWASP recommends least privilege and warns that agents can misuse tools or escalate privileges.
4. Treat retrieved documents and web content as untrusted input
Prompt injection is not limited to text a user types directly. It can be embedded in a webpage, document, email, code comment, or project record that a model is asked to summarize or use. OWASP defines prompt injection as a vulnerability that lets attackers manipulate an LLM application’s behavior with malicious input that changes its intended output.
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesSeparate instructions you intend the model to follow from quoted or retrieved material. Make it clear which content is data to analyze, and validate model output before relying on it. This helps communicate boundaries, but it is not a guarantee that the model will ignore hostile content. Do not let a response to untrusted material automatically trigger a sensitive action.
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5. Require approval before consequential actions
Put a person between an agent and actions that can cause harm. Before approving a deletion, file write, message, installation, or settings change, review the exact action and its target—not just the agent’s summary of what it plans to do.
Keep high-impact actions outside the agent’s unsupervised control. Where possible, configure the surrounding software to require explicit authorization for sensitive tool operations. OWASP’s AI Agent Security guidance recommends authorization and human oversight for consequential actions.
6. Keep secrets out of prompts, notebooks, and source code
Do not paste passwords, API keys, private tokens, or other credentials into prompts. Avoid saving them directly in source files or notebooks, which can be copied, shared, backed up, or exposed alongside the surrounding material.
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7. Protect model files, datasets, prompts, and logs on disk
Local data can still be exposed through an account with excessive access, an unprotected disk, or retained files. Restrict access to sensitive model artifacts, datasets, prompts, logs, and intermediate outputs. Use your operating system’s disk-encryption capability when sensitive material is stored on the computer.
Check whether the interface or related tools retain conversations, cached embeddings, temporary files, or diagnostic data, and where those files are kept. Remove retained artifacts that are no longer needed when the software permits. OWASP recommends encryption at rest for model weights and datasets, restricting access to logs and intermediate outputs, and clearing temporary artifacts where supported.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.8. Isolate experiments with files or models you do not trust
Use a sandbox or isolated environment for untrusted model conversion, evaluation, or fine-tuning where practical. Restrict its filesystem access and network egress so that a process handling an untrusted artifact cannot freely reach personal files or outside services.
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9. Limit resource use and watch for unexpected activity
Where your server or agent provides controls, set sensible limits on requests, concurrency, compute, and retries. These limits can help contain runaway workloads or excessive use of an exposed service. Monitor for activity that does not fit your normal use, such as unexpected request volume or tool calls, and use alerts if your software supports them.
OWASP recommends rate limits, per-workload resource limits, monitoring, and alerts for abnormal usage. The available controls vary by runtime, so verify what your software actually enforces rather than assuming a limit exists.
10. Verify security-critical output independently
A locally running model can still produce incorrect or manipulated advice. If a response recommends a security change, check it against trusted documentation for the relevant software and version. If it produces code or a command, inspect what it does and what files, accounts, or services it can affect before running it.
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Do not let a convincing answer substitute for verification. OWASP’s AI security guidance discusses hallucination, misinformation, and overreliance, and recommends limiting the impact of unwanted model behavior.
Which protections matter most for your setup?
Start with what the model can reach and do, then check how data and services are exposed. A standalone chat interface, a system that retrieves documents, and an agent that can take actions have different risk profiles. Use these questions to assess the setup you actually run:
- Model: Can you identify its source and check any integrity information provided?
- Network: Is the inference interface reachable only from the computer, or can other devices connect? If access is shared, what authentication and authorization protect it?
- Permissions: Which files, tools, and destinations can the model or agent access? Can access be narrowed or made read-only?
- Untrusted input: Does the setup read webpages, documents, email, or other outside content? Are actions based on that content reviewed?
- Storage: Where are prompts, logs, embeddings, temporary files, and diagnostics retained, and who can access them?
- Operations: Can you limit requests and workload, and notice unexpected usage?
OWASP’s Secure AI/ML Model Ops, LLM Prompt Injection Prevention, AI Agent Security, AI Security Overview, and Large Language Model Security Verification Standard materials cover these concerns. Runtime defaults are software- and version-specific; check the documentation for the one you use.
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