There's a security conversation happening in boardrooms and security teams right now that wasn't happening three years ago: our AI systems can be attacked, and we don't really know how to test them.
It's a legitimate concern. Organizations are embedding large language models into customer-facing products, internal tools, HR systems, legal research platforms, code generation pipelines, and customer support automation. They're building AI agents that can browse the internet, write and execute code, send emails, query databases, and take actions in external systems. They're using retrieval-augmented generation (RAG) to connect LLMs to their proprietary data. And they're doing all of this at a pace that has significantly outrun their security testing practices.
The problem is that AI systems — particularly LLMs and AI agents — fail in ways that traditional software doesn't. You can't just run a vulnerability scanner against them. They don't have CVEs for prompt injection. There's no patch for a model that leaks sensitive training data when asked the right question. And the OWASP LLM Top 10 — the industry's emerging standard for AI security risks — describes attack vectors that most penetration testers have never tested for.
InTechsters provides specialized AI security testing — AI VAPT — for organizations building, deploying, or integrating AI and LLM systems. Our assessments combine manual adversarial testing by experienced security professionals with AI-specific testing frameworks to evaluate your systems against real-world attack scenarios — not theoretical ones.
We use an off-the-shelf LLM from OpenAI or Anthropic. Do we still need AI VAPT?
Yes. The model provider secures the model infrastructure — but your application, how you integrate the model, what tools you expose to it, how you handle its outputs, and what data you allow it to access are entirely your responsibility. Most AI security risks live in the application layer, not in the model itself.
How is AI VAPT different from traditional application penetration testing?
Traditional VAPT tests for vulnerabilities like SQL injection, XSS, authentication bypass, and misconfigurations. AI VAPT tests for a fundamentally different class of vulnerabilities — prompt injection, model manipulation, data extraction through adversarial prompting, excessive agency, and AI-specific supply chain risks — that require specialized knowledge and techniques.
We're still in development. Is it too early to test?
It's actually the best time. Security testing during development is dramatically cheaper and faster than remediation after deployment. We offer pre-deployment AI security reviews that integrate into your development lifecycle.
Do you test AI systems built on models we host ourselves?
Yes. Whether your AI system uses a hosted API (OpenAI, Anthropic, Google, AWS Bedrock, Azure OpenAI) or a self-hosted model (Llama, Mistral, Falcon, or custom fine-tuned), we test the full stack.
Test Your AI Systems Before Attackers Do → Contact InTechsters