Who is CyberImmune best for?

Startups and mid-market technology teams that need credible VAPT, compliance readiness, cloud security, AI security, and customer review support.

How quickly can we start a VAPT assessment?

Most scopes can begin after discovery, asset list, and access handoff. Common scopes can move quickly once access is ready.

Can you support both VAPT and compliance?

Yes. We can validate security risks, support remediation, organize evidence, and coordinate readiness activities across major frameworks.

What AI systems can you assess?

LLM applications, AI agents, workflows, prompt injection paths, sensitive data exposure, unsafe automation logic, and AI API abuse paths.

Where is CyberImmune based?

CyberImmune is headquartered in Toronto, Canada, with distributed delivery across Canada, the United States, and India.

AI agent security testing beyond prompt injection

Prompt injection is only one layer

Prompt injection matters, but AI security testing should not stop there. Modern AI applications call tools, retrieve documents, trigger workflows, access APIs, and make decisions based on context that can be manipulated.

Once an AI system can take action, the assessment needs to cover identity, authorization, workflow boundaries, data exposure, logging, and failure modes.

Test what the agent can reach

Start by mapping the tools, APIs, documents, databases, and external services available to the agent. Then validate whether the agent can be pushed into actions outside the intended business workflow.

Important test areas include cross-tenant data exposure, indirect prompt injection, unsafe tool invocation, sensitive context leakage, excessive permissions, and weak human approval gates.

Validate abuse paths, not only model behavior

AI risk often appears in the surrounding product architecture rather than the model itself. Authentication flows, API permissions, file upload behavior, retrieval pipelines, and audit logs all shape the real-world risk.

A useful AI security assessment combines application security testing with AI-specific abuse cases.

Keep evidence clear for product and security teams

AI findings should include reproducible prompts or interaction steps, affected workflows, business impact, and remediation guidance. The goal is to help teams improve guardrails without blocking useful AI features.