In the early 2000s, SQL injection quietly devastated a generation of applications. Developers were moving fast, frameworks were immature, and nobody anticipated what happened when user input touched a database query unchecked. The result was predictable in hindsight and catastrophic practice.
We're in that exact moment again. The vulnerable layer isn't a database this time. It's your AI model.
The attack is called prompt injection and it's deceptively simple. An AI receives a system prompt defining its behavior, then receives user input. The attack occurs when that input overrides or hijacks the original instructions.
With a basic chatbot, this is embarrassing. With an agentic AI, it's a breach.
Agentic systems don't just respond to the act. They read files, send emails, call APIs, and execute code. A successful injection doesn't yield a text response; it yields unauthorized access, data exfiltration, and lateral movement through connected systems.
The structural parallel to SQL injection is exact: a trusted system passes context to a powerful executor that can't reliably distinguish instructions from injected data. Attackers exploit that gap with full system privileges.
Three variants exist direct injection (malicious user input), indirect injection (poisoned documents or emails the agent reads), and multi-agent injection (compromised instructions propagating across agent pipelines).
Traditional security tools, WAFs, SAST, and DAST were built for code vulnerabilities. Prompt injection lives in runtime behavior. That's exactly the gap Vigilnz was built to close.
Viginz gives security teams real-time visibility into what their AI agents are doing monitoring tool calls, detecting injection attempts in live context windows, and flagging behavioral deviations before they become incidents. Think of it as runtime threat detection, purpose-built for agentic AI.
The organizations treating this seriously now won't appear in next year's breach reports.
Agentic AI is inevitable. Agentic risk is optional.