Don’t Let Your AI Go Rogue: Our New Security Audit for Agentic Systems
- FYEO
- Jul 10
- 3 min read
Updated: Jul 11

Key Points
Announcing our new Agentic AI Security Services, designed for modern LLM agents and RAG pipelines.
Agentic AI systems introduce new risks—prompt injection, tool misuse, context poisoning, and memory leaks.
Our audit covers architecture, code, and behavior through a structured 3-part methodology.
Supports popular frameworks like LangChain, AutoGen, CrewAI, and custom stacks.
Deliverables include threat models, code hardening plans, and red team simulations.
Introduction
Agentic AI is no longer a novelty—it’s becoming the backbone of how enterprises automate complex tasks. Autonomous agents that plan, remember, and act on your behalf are revolutionizing workflows. But they're also exposing new, often invisible attack surfaces.
That’s why we’re launching our Agentic AI Security Services—a hands-on, structured offering that brings real security to real autonomy. Built from the ground up for today’s agent stacks (LangChain, AutoGen, CrewAI, and beyond), our services identify and mitigate the unique risks these systems introduce.
You don’t need to imagine what could go wrong. We simulate it, diagnose it, and help you fix it—before it hits production.
Why Agentic AI Systems Are a New Security Frontier
Agentic systems are not traditional software—they’re autonomous digital workers. They reason, call tools, and persist memory. Most importantly, they adapt. But that adaptability can make them dangerously unpredictable.
They also inherit risks you’ve probably never encountered in other software environments:
Prompt injection that silently hijacks an agent’s instructions
Planning subversion that derails logical task execution
Tool misuse via shell, API, or webhook access
Context poisoning from adversarial or over-inclusive RAG documents
Memory leaks across sessions or users
These are not theoretical risks. These are real-world vulnerabilities. If your agents touch production infrastructure, customer data, or internal systems, the time to assess them is now.
What Our New Security Service Covers
Our offering consists of a comprehensive three-part audit—modeled after proven red teaming and software assurance practices, but tailored for LLM-based agents.
Threat Modeling
We start by breaking down your agent’s architecture, analyzing how its components interact and where risks accumulate.
What We Look At
Agent architecture mapping (memory, tools, plans, I/O)
STRIDE threat modeling (spoofing, tampering, info leaks, etc.)
RAG-specific risks like adversarial retrieval, context overflow, and injection
Deliverables:
Annotated system diagram
Threat matrices and abuse scenarios
RAG-specific risk appendix
Code Review
Next, we dig into your codebase—manually. Our team audits orchestration logic, tool integration, memory handling, and prompt construction.
What We Review
Prompt safety: no unsafe interpolation or prompt duplication
Tool invocation: gated execution, sandboxing, parameterization
Memory logic: safe reads/writes, no hallucinated state or loops
RAG chain: secure chunking, retrieval filtering, injection defense
Deliverables:
Code vulnerability report
Severity breakdown
Fix recommendations + hardening checklist
Red Team Simulation
Finally, we take off the gloves. Our team launches controlled adversarial attacks to simulate how a real threat actor might exploit your system.
What We Simulate
Prompt injection to override instructions or access memory
Shell/API command abuse
RAG-based exfiltration and retrieval manipulation
Cross-user memory leaks in shared embeddings
Deliverables:
Red team logbook with payloads and outcomes
Replayable exploit steps
Kill-chain diagrams and impact scores
Supported Frameworks and Use Cases
This service is designed for any LLM-powered system with autonomous behavior, including:
LangChain, CrewAI, AutoGen-based stacks
Custom Python/TypeScript agent frameworks
RAG pipelines grounded in enterprise data
Workflow automation bots
Security, DevOps, or customer support copilots
If your agents have tool access or memory, they’re in scope.
What You’ll Receive
Every engagement includes:
A Threat Model Report with trust diagrams and threat matrices
A Code Audit Report with annotated findings and fix instructions
A Red Team Report with logs, exploit walkthroughs, and risk prioritization
You’ll walk away with a hardened system, a clear understanding of risk, and the confidence to go live.
Let’s Talk Security
If you’re deploying agentic AI systems in production, you already know the stakes. Now there’s a proven way to test them, harden them, and move fast without fear.
Or get in touch to schedule your review.
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