LLM Security Best Practices: 15 Ways to Secure LLMs in 2026

Quick Answer: This guide explains 15 practical ways to secure LLM applications in 2026, with particular attention to prompt injection, sensitive information disclosure, supply chain vulnerabilities, excessive agency, RAG security, and AI runtime security.

Large language models (LLMs) can process sensitive information, generate code, access business systems, and increasingly interact with external tools. That makes LLM security a broader problem than simply protecting the model itself. Organizations need to secure the data, prompts, applications, APIs, plugins, retrieval systems, permissions, and infrastructure surrounding the model.

LLM security best practices provide a practical framework for reducing these risks. The most effective approach combines secure architecture, input and output controls, least privilege access, monitoring, adversarial testing, and incident response.

What Are LLM Security Best Practices?

LLM security best practices are the technical, architectural, and operational controls used to protect large language models and the applications built around them from attacks, misuse, data exposure, and unintended behavior.

A secure LLM deployment should use a defense-in-depth approach that treats the model as an untrusted, probabilistic component rather than a standard deterministic backend service. The application should enforce security boundaries independently, rather than assuming the model will always follow instructions correctly.

OWASP’s current GenAI security work identifies risks including prompt injection, sensitive information disclosure, supply chain susceptibility, data and model poisoning, improper output handling, excessive agency, system prompt leakage, vector and embedding weaknesses, misinformation, and unbounded consumption.

Why LLM Security Matters

Traditional applications generally follow predictable program logic. LLM applications are different because model behavior is influenced by natural language inputs, retrieved content, system teachings, tools, conversation history, and probabilistic generation.

An attacker may therefore target not only the model but also the application surrounding it. A successful attack could expose confidential information, manipulate business workflows, trigger unauthorized tool actions, increase infrastructure costs, or introduce malicious content into downstream systems.

NIST’s Generative AI Profile specifically discusses risks such as prompt injection, indirect prompt injection, data poisoning, and information disclosure, highlighting the need to adapt conventional cybersecurity practices for generative AI systems.

15 LLM Security Best Practices for 2026

Perform LLM Threat Modeling Before Deployment

Start security work before connecting an LLM to production systems.

• User inputs
• System prompts
• Model APIs
• RAG pipelines
• Vector databases
• Plugins and tools
• External APIs
• Exposed data stores
• Authentication systems
• Logging infrastructure

Identify what an attacker can control, what the model can access, and which actions can affect external systems.

Treat User Input as Untrusted

Never assume that a user’s prompt is safe because it looks like ordinary language. Attackers can deliberately construct inputs designed to override instructions, extract data, manipulate outputs, or influence connected tools. These LLM prompt injection attacks may be direct or arrive indirectly through content that an application retrieves.

NIST distinguishes direct prompt injection from indirect prompt injection, in which malicious instructions can be embedded in external data that an LLM-integrated application later recovers. Use validation, input boundaries, content classification, and permission controls rather than relying on the model to reject malicious instructions.

Treat System Prompts as Public Data

A system prompt should not be treated as a secret security boundary. System prompt leakage can expose internal instructions, business logic, tool descriptions, or other information architects expect the model to keep private. OWASP includes System Prompt Leakage as a dedicated LLM security risk.

Avoid putting passwords, API keys, authorization decisions, or other secrets into prompts. Security-critical rules should exist in application code and infrastructure, where they can be enforced independently of model behavior.

Sanitize and Partition Inputs

Separate different types of range rather than putting everything into a single, undifferentiated prompt. For example, an application can distinguish:

Trusted instructions → User Content → Retrieved Documents → Tool Results

This makes trust boundaries easier to enforce. External documents, web pages, emails, and retrieved RAG content should be treated as potentially untrusted. Exact partitioning can reduce the likelihood that malicious content is interpreted as a higher-priority instruction. OWASP’s guidance specifically emphasizes the segregation and identification of external content as part of defenses for LLM applications.

Apply Least Privilege to AI Agents

An LLM should receive only the permissions required for its intended task. For example, a client support agent may need to read order information but should not automatically receive permission to delete accounts, issue unrestricted refunds, access payroll systems, or send arbitrary emails.

• Scoped API tokens
• Role-based permissions
• Separate service accounts
• Tool-specific permission
• Read-only access where possible
• Human approval for high-risk actions

OWASP recommends enforcing privilege controls and least-privilege access rather than allowing the model to control privileged functionality directly.

Validate Every Model Output

Never assume that generated output is safe simply because the model produced it. If an LLM generates SQL, HTML, commands, API parameters, or structured data, validate that output before passing it to another system. For instance, an application should not execute model-generated commands directly on a production server without appropriate validation and authorization. This protects against improper output handling, a major risk identified by OWASP.

Protect Sensitive Information

LLM applications may process customer diaries, financial information, credentials, proprietary documents, source code, or other sensitive data. Apply data minimization before information reaches the model. 

• Data classification
• Redaction
• Tokenization
• Access controls
• Encryption
• DLP monitoring
• Retention limits

Also consider what information appears in prompts, conversation history, logs, vector databases, and model outputs.

Secure RAG and Vector Databases

Retrieval Augmented Generation (RAG) introduces another attack surface. A malicious or incorrectly permissioned document can influence model responses. Weak access controls around vector databases can also expose information that a user should not be allowed to retrieve.

Secure RAG Systems By:

• Enforcing document-level permissions
• Validating ingestion sources
• Tracking document provenance
• Separating tenants
• Monitoring unusual retrieval behavior
• Removing unauthorized content
• Testing retrieval boundaries

OWASP’s 2025 guidance specifically added Vector and Embedding Weaknesses as a major LLM application risk.

Secure the AI Supply Chain

LLM applications depend on more than the model. Their supply chain may include datasets, open-source libraries, model weights, embedding models, containers, plugins, APIs, and third-party services.

A compromised dependency can introduce vulnerabilities without changing the application’s visible functionality. Use dependency inventories, provenance checks, version controls, vulnerability scanning, trusted sources, and integrity verification where appropriate.

Test for Prompt Injection and Adversarial Attacks

Security testing should intentionally attempt to break the application’s trust boundaries.

• Direct prompt injection
• Indirect prompt injection
• Jailbreak attempts
• Exposed data extraction
• Malicious RAG documents
• Tool manipulation
• Instruction conflicts
• Excessive permissions
• Unexpected output formats

Do not test only whether the model refuses unsafe prompts. Test whether an attack can cause real downstream impact. Google’s Secure AI Framework also emphasizes AI security assessments and red teaming as ways to evaluate AI systems against attacks.

Implement AI Runtime Security and Monitoring

Security does not end when an LLM application reaches production. Runtime monitoring can help detect unusual behavior such as sudden increases in requests, repeated extraction attempts, abnormal tool usage, unexpected data access, or suspicious prompt patterns.

AI runtime security should connect model behavior to application-level telemetry. Logs should provide enough context to investigate incidents without unnecessarily storing sensitive user information.

Control Unbounded Consumption

LLM applications can consume substantial compute resources, especially when attackers submit unusually expensive requests or trigger repeated tool and model calls.

Implement:

• Rate limits
• Request quotas
• Token limits
• Timeout rules
• Maximum tool call limits
• Cost monitoring
• Abuse detection

OWASP’s Unbounded Consumption category expands the traditional denial-of-service concept to include resource management and unexpected costs associated with LLM applications.

Reduce Hallucination and Misinformation Risk

Security is not only about malicious detractors. Incorrect model output can also become a security problem when users or automated systems trust it without verification. For high-impact applications, use:

• Retrieval from trusted sources
• Output validation
• Confidence-aware workflows
• Human review
• Grounding techniques
• Clear delay handling

OWASP identifies misinformation as a significant risk in LLM applications because inaccurate outputs can influence decisions and downstream processes.

Prepare an LLM Incident Response Plan

Organizations should know what to do when an LLM application is compromised. An LLM incident response plan should define how to:

1. Detect and confirm the incident.
2. Disable affected tools or integrations.
3. Revoke exposed credentials.
4. Preserve relevant logs and evidence.
5. Determine what information was accessed.
6. Contain the simulated system.
7. Restore secure functionality.
8. Review the root cause and improve controls.

This is particularly important for AI agents because an attack may involve both model behavior and external systems.

Continuously Review and Update Security Controls

LLM security is not a one-time configuration task. Models, prompts, dependencies, retrieval sources, APIs, agents, and attack techniques change over time. Security controls should therefore be reviewed after significant changes to the architecture or model.

For 2026 deployments, teams should regularly review their current security frameworks rather than relying indefinitely on an outdated checklist. The latest OWASP GenAI LLM Top 10 provides an updated view of LLM security risks, including prompt injection, sensitive information disclosure, bloated agency, and AI application security. For readers of AiSecMaster, combining these recommendations with practical LLM security best practices, threat modeling, AI runtime security, and continuous security testing can provide a stronger foundation for protecting modern LLM applications.

LLM Security Best Practices GitHub Resources

GitHub can be useful for researching practical LLM security techniques, test cases, security frameworks, and open source tooling. However, organizations should distinguish between a repository being useful for research and a component being safe for production.

For LLM security best practices, GitHub research: prioritize repositories maintained by recognized security organizations or established projects; review commit history and documentation; inspect dependencies; and evaluate whether the project is actively maintained.

The OWASP GenAI Security Project is particularly relevant because its work includes open-source resources on LLM and generative AI security.

OWASP and NIST for LLM Security

OWASP provides practical guidance on application security for LLMs and generative AI systems. At the same time, NIST offers a broader risk management perspective through its AI Risk Management Framework and Generative AI Profile.

Together, they can help teams connect technical controls with a wider AI risk management process. NIST’s AI RMF Generative AI Profile is designed to help organizations incorporate trustworthiness considerations into the design, development, use, and evaluation of generative AI systems. For current work, organizations should consult the latest OWASP GenAI publications, as the project continues to evolve alongside LLM and agentic AI architectures.

Common LLM Security Mistakes to Avoid

Several mistakes repeatedly weaken otherwise well-designed LLM applications. Relying on prompts for authorization is one of the most important. A model instruction, such as “never reveal customer data,” should complement application-level authorization, not replace it.

Another mistake is giving an AI agent broad permissions because they make automation easier. A compromised agent with access to email, databases, files, and external APIs can create a much larger blast radius than a chatbot with no external actions.

Finally, teams should avoid testing only the model. LLM security is application security plus model-specific threats. The surrounding APIs, identity systems, databases, retrieval pipeline, tools, and infrastructure must all be included in the security assessment.

Conclusion

Effective LLM Security Best Practices are built around layered controls rather than a single prompt, filter, or protection product. Treat the model as an untrusted component, protect sensitive information, restrict permissions, secure RAG and supply chain components, validate outputs, monitor production behavior, and continuously test for new attack paths.

For AiSecMaster, the practical takeaway is simple: secure the entire LLM application, not just the model. As LLMs become more connected to business data and more autonomous, application-level authorization, runtime monitoring, threat modeling, and incident response become just as important as model-level defenses.

Teams should use current OWASP and NIST guidance as starting points and then build controls tailored to their own data, users, integrations, and business impact.

Frequently Asked Questions (FAQs)