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AI Red Teaming Services

ProArch provides AI Red Teaming services that help organizations secure LLM apps, copilots, RAG pipelines, and AI agents through adversarial testing, architecture assessment, and secure code review.

 

Introduction

What is AI Red Teaming?

AI Red Teaming has the same goal as traditional testing but covers a different attack surface. It extends traditional security testing into LLM penetration testing, chatbot evaluation, prompt injection testing, and adversarial testing for AI interfaces.

What it assesses: LLMs, copilots, RAG pipelines, AI agents, tools, and MCP servers.

What it finds: Prompt injection, jailbreaks, data leakage, agent and tool abuse.

Frameworks: OWASP Top 10 for LLM Apps, MITRE ATLAS, NIST AI RMF.

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AI SECURITY TESTING

Why Your AI Needs its Own Red Team

Your AI has a new attack surface, and your Penetration testing wasn't built to find it.

Most organizations already run penetration tests on their applications. That testing still matters, but AI is not a single attack surface.

AI interfaces and agents combine prompts, models, data sources, tools, permissions, and APIs with its own failure modes. Hence, AI systems require dedicated AI penetration testing services to uncover risks like prompt injection, data leakage, tool abuse, and unintended actions, to show weaknesses and how to fix them.

PROARCH AI RED TEAMING METHODOLOGY

Attack it. Explain why it failed. Show where to fix it.

Every engagement follows the same six-phase path. The tier you choose decides which phases are in scope.

Step 01

Discover & Threat Model

We map your models, prompts, RAG sources, tools and trust boundaries, and build a threat model using MITRE ATLAS.

 
Step 02

Architecture Assessment

We evaluate data flows, architecture, identity & access management model, guardrails, logging & monitoring controls to find out why attacks worked.

 
Step 03

Secure Code Review

We analyze how your AI integration code builds prompts, calls tools and handles outputs.

 
Step 04

Adversarial Testing

We run manual and automated attacks, including injection, jailbreaks, data extraction, agent and tool abuse.

 
Step 05

Report & Readout

You get risk-rated findings, reproducible proofs of concept and a prioritized roadmap, with both executive and technical readouts.

 
Step 06

Retest

We retest to confirm your fixes work as intended & continue to hold up when the AI system is challenged, manipulated, or exposed to new attack paths.

 

AI RED TEAMING & AI SECURITY ASSESSMENT SERVICES

What Level of AI Security Testing Does Your Organization Need?

ProArch offers three tiers of AI Red Teaming: Surface (outside-in attack testing), Structural (attack testing plus security architecture review) and Source (adds secure code review). Each AI Red Teaming tier includes a threat model, executive readouts, and one retest.

Tier 01 : Surface

Adversarial AI Red Team

Tests for prompt injection, jailbreaks, data leakage, agent abuse, tool, and MCP misuse.

Teams preparing to launch AI applications that need to meet compliance requirements, support investment decisions, or pass customer review.

 

Tier 02 : Structural

AI Attack Testing Plus Security Architecture Review

Combines adversarial testing with a review of trust boundaries, identity, guardrails, logging, monitoring, and AI platform configuration

Teams that need to understand why attacks worked, where the design is exposed, and what fixes to prioritize first.

 

Tier 03 : Source

AI Security Testing With Secure Code Review

Adds AI integration code review & implementation analysis to identify weaknesses in prompts, tool calls, AI logics, and workflows.

Organizations building AI solutions in-house that require precise, implementation-level remediation guidance.

what you get

What Do You Get from a ProArch AI Red Team Assessment?

Deliverable Surface Structural Source
Exec Summary      
Findings & PoCs      
Attack Paths      
Roadmap & Retest      
Architecture Assessment      
Code Review      

CHOOSING YOUR AI RED TEAMING TIER

How Do I Choose the Right AI Security Testing Tier?

Match the depth of testing to what you need to prove, explain, or fix. Which depth is right for you?

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Need evidence for a launch gate or a customer? Choose Surface.

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Need root causes and a remediation roadmap? Choose Structural.

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Building AI in-house and need fixes traced to the exact line of code? Choose Source.

Why ProArch

Why Choose ProArch for AI Red Teaming

AI Red Teaming is part of ProArch's established offensive security practice, not a side project.

  • ProArch brings offensive security depth through the same team behind ProArch’s application, API, cloud, and red team engagements.
  • Specialized in testing the full AI ecosystem and not just the model.
  • We combine AI security tools with expert-led attack testing.
  • Maps risks to root causes, clear fixes, and a retest to confirm they hold.
  • Aligns findings to remediation, compliance, and AI risk management.
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Knowledge & Insights

Understand the Threats. Test Your Defenses.

Cloud identity penetration testing of Microsoft Entra ID environment showing token attack simulation and conditional access validation
Case Study

How Far Could an Attacker Get? Validating Cloud Identity Resilience at MDC Research

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Blog

Most Common Weaknesses ProArch's Penetration Testing Uncovers

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Blog

Why Penetration Testing Early in the Year Pays Off?

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View All Resources
 
 

Ready to test your AI before attackers do?

ProArch helps you understand whether your AI can expose sensitive data, be manipulated into unsafe actions, or create unintended business risk, so your teams can move faster with confidence.

Talk to an AI Security Expert

Frequently Asked Questions

How do I know if my AI application needs red teaming?

Your AI application should be considered for red teaming if it uses LLMs, copilots, RAG pipelines, AI agents, tools, APIs, or sensitive data.

Testing becomes particularly important when the AI can access business systems or take actions, because these capabilities introduce risks such as prompt injection, data leakage, and tool abuse.

When should we conduct AI red teaming, before or after deployment?

Ideally, AI red teaming needs to be conducted before launching an AI application, particularly when you need security evidence to support launch, compliance requirements, investment decisions, or customer reviews.

Testing should also continue as the application changes.

ProArch includes a retest to confirm that identified fixes work when the AI system is challenged again.

Can you red team AI agents that can take actions or use tools?

Yes. ProArch tests AI agents, tools, and MCP servers to identify risks such as agent abuse, tool misuse, prompt injection, and unintended actions.

Testing can help determine whether an attacker could manipulate an AI agent into using its available capabilities in ways the organization did not intend or authorize.

What is the difference between AI red teaming and traditional penetration testing?

Traditional penetration testing finds vulnerabilities in code, configuration, and design across web, mobile, SaaS, and API.

AI introduces additional attack surfaces involving prompts, models, data sources, tools, permissions, and APIs.

AI red teaming extends security testing into areas such as

  • LLM penetration testing,
  • AI interface testing,
  • Prompt injection testing,
  • Jailbreaks, data leakage, and
  • Adversarial testing of AI agents and tools

What will we receive after an AI red team assessment?

Every ProArch AI Red Teaming tier includes;

  • a threat model
  • executive summary
  • risk-rated findings
  • reproducible proofs of concept
  • identified attack paths
  • prioritized remediation guidance
  • executive and technical readouts

Structural tier adds security architecture assessment, and the Source tier also includes secure code review.

How often should AI applications be red teamed?

There is no single testing frequency that fits every AI application.

A practical approach is to reassess when significant changes introduce new attack paths, such as changes to models, RAG sources, tools, permissions, APIs, or AI agent capabilities.

Retesting is also important after remediation to confirm that identified fixes hold up under attack.

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