Your AI Product Ships Fast. Is It Secure?
AI startups face attack surfaces that traditional security testing was never designed for. We test LLM-powered applications, AI agents, prompt injection resistance, and the vibe-coded infrastructure underneath it all.
Why this sector gets targeted
AI startups are building products with novel attack surfaces that the security industry is still catching up to. LLM-powered applications are vulnerable to prompt injection, jailbreaking, and data extraction attacks that bypass traditional input validation. AI agents with tool access, API keys, and database credentials create privilege escalation paths that did not exist 18 months ago. The rapid development pace - often using AI coding tools like Cursor, Copilot, and Claude to build the product itself - compounds the risk with vibe-coded infrastructure that may contain hardcoded secrets, broken authentication, and insecure defaults. Meanwhile, enterprise buyers evaluating AI products are increasingly requiring evidence of security testing before procurement, making a pentest a revenue enabler rather than just a risk mitigation exercise.
Who we work with here
What we usually run
Scoped for this sector rather than sold as a catalogue. Each one is fixed scope with retest included.
AI Agent Penetration Testing
Test your agents for prompt injection, tool abuse, and privilege escalation.
How it runs →Web Application Penetration Testing
Your AI product still runs on a web application with traditional attack surfaces.
How it runs →API Penetration Testing
AI products are API-heavy. Test your inference endpoints, webhooks, and integrations.
How it runs →Vibe Coding Security Reviews
If your product was built with AI coding tools, verify the code is secure.
How it runs →SOC 2 Penetration Testing
Enterprise AI buyers expect SOC 2 compliance. Get audit-ready reports.
How it runs →Security Code Reviews
Find logic flaws in your AI pipeline, data handling, and authentication code.
How it runs →What you get working with Lorikeet
- Specialized testing for LLM applications, AI agents, and prompt injection attack vectors
- Experience reviewing AI-generated codebases built with Cursor, Copilot, and Claude
- Reports designed to satisfy enterprise buyers, SOC 2 auditors, and investor due diligence
Asked on almost every call
Prompt injection is an attack where malicious input manipulates your LLM into ignoring its system prompt and following attacker instructions instead. If your AI product processes any user input, you are potentially vulnerable. We test for direct injection, indirect injection via retrieved content, and multi-step attacks that chain prompt manipulation with tool access.
AI coding tools generate functional code fast but consistently produce insecure patterns - hardcoded credentials, missing authorization checks, verbose error messages, and insecure defaults. Our vibe coding reviews are specifically designed to catch what LLMs get wrong.
Yes. AI agents introduce attack surfaces that traditional pentesting does not cover - tool call authorization, credential exposure in agent context, autonomous decision-making boundaries, and the ability to chain actions in ways the developer never intended. We test the agent itself, its tool integrations, and the boundaries of what it can do.
Before your first enterprise customer, before your SOC 2 audit, or before any fundraise where investors will ask about security. If your AI product handles customer data, has tool access to external systems, or processes sensitive information, you should be testing now.
We always coordinate testing scope and boundaries with your engineering team. For AI products, we use controlled test inputs, sandboxed environments when available, and escalation procedures for any unexpected behavior. We will never run destructive tests against production AI systems without explicit authorization.
Scope it in one call
Tell us what is in scope and we come back with a fixed price and a start date.