Shipping an AI feature is exciting, but LLM applications introduce risks that standard release checklists miss. Use this practical checklist to reduce the most common AI security issues before you launch — and to keep them reduced as your application evolves.
Map your AI attack surface
Before testing anything, inventory what you are exposing: LLM endpoints, agents, the tools and APIs they can call, the data sources they read, and the system prompts behind them. You cannot secure an AI feature whose moving parts you have not mapped, and these systems often connect to more than teams realise.
Test for prompt injection and data leakage
Actively attempt direct and indirect prompt injection, and try to coax the model into revealing sensitive data, secrets or its system prompt. These are the most common and most damaging LLM risks, so they belong at the top of any pre-launch test plan.
Constrain agents and validate output
Apply least privilege to any agent or tool the model can use, and require confirmation for high-impact actions. Treat model output as untrusted: validate and sanitise it before it is rendered, executed or passed to other systems, to prevent classic flaws like XSS and SSRF arising from AI responses.
Protect the supply chain
Consider the provenance of your models, plugins and training or retrieval data. Poisoned data, compromised plugins and untrusted third-party models all introduce risk, so vet what you depend on and monitor for changes.
Monitor and re-test continuously
AI features are not static: prompts change, models are updated, and new integrations are added. Treat security as ongoing — monitor behaviour in production and re-test regularly, because each change can reintroduce risk you previously closed.
How Attack Insights and DarkThreatX help
Attack Insights AI Security works through exactly this checklist — mapping your AI attack surface and testing it against the OWASP Top 10 for LLMs, with verified findings. DarkThreatX adds threat intelligence from over 100 billion records so your AI defences are informed by real attacker activity.
Common mistakes to avoid
LLM security checklists fail when teams treat them as one-time launch gates rather than ongoing practice; prompts, models and integrations change constantly and each change can reintroduce risk. A frequent mistake is mapping only the obvious endpoints and missing the agents, tools and data sources the model can reach. Some test for prompt injection but forget data leakage, output handling or the AI supply chain. Others grant agents broad permissions and trust model output without validation. And many never monitor AI features in production, so issues surface only after exploitation. The reliable approach maps the full AI attack surface, tests against the OWASP LLM Top 10 adversarially, constrains agents, validates output, vets the supply chain, and monitors and re-tests continuously.
Key takeaways
- Map every part of your AI attack surface first.
- Prioritise prompt-injection and data-leakage testing.
- Constrain agents and treat model output as untrusted.
- Vet your model, plugin and data supply chain.
- Monitor and re-test continuously as the app evolves.
Request a demo to see continuous, validated penetration testing in action.
