AI Security: The OWASP LLM Checklist for Founders

AI security is no longer a post-Series A concern. Enterprise buyers, compliance teams, and technical diligence reviewers now expect seed-stage AI products to address the OWASP Top 10 for LLM Applications before signing pilots. This checklist translates each OWASP category into concrete controls founders can implement — not a security audit, but the minimum viable security posture that survives a customer security questionnaire.

LLM01: Prompt Injection

Prompt injection is the SQL injection of the AI era — adversarial inputs that override system instructions, exfiltrate data, or trigger unintended tool actions. Direct injection comes from user input; indirect injection hides in retrieved documents, emails, or web content the model processes.

• Input sanitization — strip or escape known injection patterns; limit input length.
• System prompt isolation — never concatenate user input into system prompts.
• Tool permission boundaries — agents can only call tools explicitly authorized for the current user context.
• Output filtering — detect when responses contain system prompt fragments or internal instructions.
• Human-in-the-loop — require approval for high-stakes actions (payments, deletions, external API calls).

LLM02: Insecure Output Handling

Model outputs are untrusted user input. Treating generated code, SQL, HTML, or shell commands as safe to execute is a critical vulnerability. OWASP's guidance emphasizes validation before any downstream action.

• Validate structured outputs against JSON schemas before processing.
• Sandbox code execution in isolated environments with no network access.
• Sanitize HTML/markdown before rendering in browsers.
• Never pass model-generated SQL directly to databases — use parameterized queries with validated identifiers only.

LLM03 & LLM04: Training Data and Model Denial of Service

Training data poisoning affects teams fine-tuning models on user data — a growing pattern at seed stage. Model DoS occurs when adversarial inputs consume excessive tokens or trigger expensive agent loops. Controls:

• Data poisoning — validate and sanitize fine-tuning datasets; monitor for anomalous training samples.
• Model DoS — per-user rate limits, token budgets per request, max agent loop iterations, and cost ceilings with graceful degradation.
• Input size limits — cap document uploads, context window usage, and concurrent requests.

LLM05 & LLM06: Supply Chain and Sensitive Information Disclosure

Supply chain vulnerabilities include compromised model weights, poisoned plugins, and vulnerable dependencies in AI frameworks. Sensitive information disclosure happens when PII, credentials, or proprietary data leak through model outputs or logs.

• Supply chain — pin model versions, audit third-party plugins, scan dependencies (Dependabot/Snyk).
• Data in context — map what PII enters prompts; implement redaction before model calls.
• Log hygiene — never log full prompts/responses containing customer data in production.
• Retrieval boundaries — enforce RBAC on knowledge bases; users should only retrieve documents they're authorized to access.

LLM07 & LLM08: Plugin and Excessive Agency

Agent systems with tool access create "excessive agency" — the model can take actions beyond what the user intended. Anthropic's tool use documentation recommends least-privilege tool design.

• Design tools with minimal scope — one action per tool, not Swiss Army knife functions.
• Require explicit user confirmation for irreversible actions.
• Implement action audit trails — who triggered what, when, with what inputs.
• Disable autonomous loops for financial, deletion, or communication actions.

The safest agent is one that asks permission before acting and logs everything it does.

LLM09 & LLM10: Overreliance and Model Theft

Overreliance occurs when users or systems trust model outputs without verification — especially dangerous in medical, legal, or financial contexts. Model theft involves extracting proprietary prompts or fine-tuned weights through API abuse.

• Overreliance — display confidence indicators, require citations for factual claims, surface uncertainty.
• Model theft — rate limit API access, monitor for systematic prompt extraction patterns, watermark outputs if applicable.
• Disclaimers — clear UX indicating AI-generated content requires human verification for high-stakes decisions.

The seed-stage security minimum

You don't need a dedicated security team at seed — but you need these artifacts before enterprise sales:

1. Data flow diagram — what data enters models, what gets logged, where it resides.
2. OWASP LLM checklist — self-assessment against all 10 categories with mitigations documented.
3. Red-team results — documented prompt injection tests with remediation status.
4. Incident response plan — one page covering detection, containment, and customer notification.
5. Access controls — RBAC on knowledge bases, API keys in secrets manager, audit logging enabled.

Technical diligence in 2026 routinely includes OWASP LLM review. Founders who can walk through this checklist confidently — with evidence, not aspirations — close enterprise pilots faster and avoid security-driven deal stalls that kill seed-stage momentum.