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Don’t Let Your AI Go Rogue: Our New Security Audit for Agentic Systems

  • Writer: FYEO
    FYEO
  • Jul 10
  • 3 min read

Updated: Jul 11

Introducing Agentic AI Security Services: Real Security for Real Autonomy
Introducing Agentic AI Security Services: Real Security for Real Autonomy

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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