LLM Security Checklist for Production Deployments in 2026

Why This Checklist Exists

You've shipped an LLM feature. Maybe it's a customer-facing chat interface, a copilot, or an agent that calls external tools. The model is responding correctly in your test cases. But have you actually verified it can't be jailbroken, manipulated into leaking your system prompt, or exploited through indirect injection?

Most developers haven't. Not because they don't care, but because there's been no fast, practical way to check without standing up a full enterprise security program.

This checklist covers what you should verify before and after you go to production with an LLM-powered feature in 2026. It's organized by category, not by priority, because all of it matters.

1. Endpoint Hardening

• ☐Your LLM API endpoint is not publicly discoverable or documented in client-side code
• ☐Rate limiting is in place to prevent automated abuse and enumeration
• ☐Request size limits are enforced to block oversized payloads
• ☐HTTPS is enforced end-to-end with no fallback to plaintext
• ☐CORS policy is locked down to expected origins only
• ☐Error responses do not expose model names, versions, or internal configuration details

Endpoint hygiene is table stakes. An attacker who can freely enumerate your API or read error messages that reveal your model version has a significant head start.

2. Authentication and Access Control

• ☐API keys or tokens are rotated on a defined schedule and not hardcoded in source
• ☐Challenge-token authentication is implemented where applicable
• ☐Each user or tenant has scoped access — no shared credentials across accounts
• ☐Authentication failures are logged with enough context to detect brute-force attempts
• ☐Your LLM backend does not accept unauthenticated requests under any condition

If you're using a hosted provider like OpenAI or Anthropic, your API key is your perimeter. Treat it accordingly.

3. Prompt Injection Testing

Prompt injection (OWASP LLM01) remains the most commonly exploited vulnerability class in LLM applications. Both direct and indirect injection need coverage.

• ☐Direct injection: verify that user-supplied input cannot override system prompt instructions
• ☐Indirect injection: verify that content retrieved from external sources (URLs, documents, tool outputs) cannot inject instructions into the model context
• ☐Test injection via all user-controlled fields, not just the primary chat input
• ☐Confirm that injection attempts return expected refusals or constrained responses, not compliance

Testing this manually is slow and incomplete. Running a structured scan across the promptinject, latentinjection, and webinjection attack groups gives you systematic coverage across documented injection patterns.

4. Jailbreak and Safety Bypass Testing

• ☐DAN-style attacks (Do Anything Now) are tested and fail
• ☐Role-play manipulation attempts do not cause the model to abandon its persona or instructions
• ☐Prefix injection and token manipulation attacks are tested
• ☐Safety bypass attempts using encoding, language switching, or hypothetical framing are covered
• ☐Attacks from the lmrc and goodside groups are included in your test suite

Jailbreaks are not theoretical. They're documented, reproducible, and actively used. If you haven't tested your endpoint against them, you don't know your actual exposure.

5. System Prompt Protection

• ☐The model cannot be prompted into revealing the contents of its system prompt
• ☐Indirect references to system prompt contents are also tested (not just direct "repeat your instructions" attempts)
• ☐System prompt leak detection is part of your pre-launch scan, not a post-incident discovery

A leaked system prompt hands an attacker your application logic, your persona instructions, and often your tool configurations. This is one of the most common findings in LLM security reviews.

6. Output Validation and Response Handling

• ☐Model outputs are validated before being passed to downstream systems or rendered to users
• ☐Structured outputs (JSON, code) are parsed safely without eval or equivalent
• ☐Outputs that trigger tool calls or agent actions are validated against an allowlist of permitted operations
• ☐Sensitive data patterns (PII, credentials, internal IDs) are screened in model responses before delivery
• ☐Streaming responses (SSE, NDJSON) are handled with the same validation logic as non-streaming responses

Streaming formats in particular are easy to skip in validation pipelines. If your endpoint returns SSE or NDJSON, make sure your response parsing handles those correctly in both testing and production.

7. Automated Scanning and Ongoing Coverage

A one-time pre-launch scan is not sufficient. Your system prompt changes. Your model version changes. New attack patterns emerge.

• ☐Automated security scans are scheduled to run at least weekly
• ☐Scans cover all active LLM endpoints, not just the primary one
• ☐Pass/fail verdicts are reviewed after each scan run
• ☐New attack vectors are incorporated into your test suite as they're documented
• ☐Scan results are stored with enough history to detect regressions

BastionLLM handles this directly. You configure your endpoint URL, payload schema, and response parsing once, then run scans across all major attack groups including promptinject, lmrc, goodside, latentinjection, prefix, and webinjection. Weekly automated scans run without manual intervention, and results come back as pass/fail verdicts with raw responses so you can see exactly what the model returned.

The Pro tier at €49/month (or €449/year), tax included, covers 3 endpoints with full attack group access, weekly scheduling, and 1 year of scan history. There's also a free tier with PDF export if you want to run your first scan before committing — how to send your manager an LLM security PDF without a credit card walks through that path.

8. Audit Logging and Reporting

• ☐All LLM requests and responses are logged with timestamps and user context
• ☐Security scan results are exported and stored in a format your team can review (PDF or JSON)
• ☐Logs are retained long enough to support incident investigation
• ☐A documented process exists for responding to a detected vulnerability or incident
• ☐Scan history is available to demonstrate ongoing security validation to stakeholders or auditors

If you're at a company that handles customer data, being able to show a history of security scans with documented pass/fail outcomes is increasingly expected. It's not just about finding vulnerabilities — it's about demonstrating that you looked.

FAQs

Ship With Confidence

This checklist won't prevent every possible attack. But working through it before you ship, and then running automated scans on a weekly basis afterward, puts you significantly ahead of most teams deploying LLM features in 2026.

The gap between "it works in testing" and "it's secure in production" is where most incidents happen. Closing that gap doesn't require an enterprise security program. It requires systematic testing.

Start with a free scan at bastionllm.com. No credit card needed.