AI-Native Firewalling: Real-Time Defense for LLM & Agentic Applications - CDA Networks
Addressing Prompt Injection, MCP Privilege Abuse, and Agentic Exploits in Real Time
- Wednesday, October 21st, 2026
- 4:00 PM IST |
- 2:30 PM GST |
- 11:30 AM BST |
- 12:30 PM CEST
Days
Hours
Minutes
Sec
Reserve Your Seat - Now!
Limited seats — register early to secure your spot.
Prophaze & CDA IT Solutions
About this Webinar
Your defenses were built for code. The attack is now language.
Teams are shipping LLM apps and MCP-connected agents faster than security can review them — vibe-coding with tool access nobody audited. Static WAFs read syntax, not intent, so natural-language payloads and indirect prompt injection walk straight past them.
Meanwhile autonomous agents are calling internal APIs on their own, often with privileges that were granted for convenience and never revisited. This session breaks down where that visibility gap actually sits, and what closing it looks like in practice.
What You'll Walk Away With
45 minutes of walkthrough and live demo, plus 15 minutes of open Q&A with the panel.
- Where static signatures fall short - Why standard WAFs fail against natural language payloads, indirect prompt injections, and contextual attacks with no known-bad syntax to match.
- Agentic AI & MCP risk - How Model Context Protocol integrations grant autonomous agents excessive privilege over internal APIs, and how to scope it down.
- Live exploit & defense demo -A real-time prompt injection attack bypassing standard guardrails, followed by Prophaze's AI-native firewall detecting and neutralizing it by intent.
- OWASP LLM Top 10 walkthrough —A working deep-dive into LLM01 Prompt Injection, supply-chain exposure, and data exfiltration vectors.
- Data leakage & shadow AI exposure - Why PII and IP fed into public or third-party AI models is becoming the next unmonitored blind spot.
- Enterprise deployment roadmap - The step-by-step framework to put real-time LLM input/output filtering in front of a production app in under 15 minutes.
