AI red teaming and AI penetration testing are not the same thing. AI penetration testing looks for every possible vulnerability to build a secure baseline. AI red teaming simulates a targeted attack to test your detection and response capabilities. Choose an AI security assessment based on your current operational maturity.
People use the terms "AI red teaming" and "AI penetration testing" interchangeably. But these two terms describe two different services.
If you hire a red team when you actually need a penetration test, you leave your system exposed. To secure your large language models (LLMs) and agentic workflows, you must understand the difference between these two tests.
This guide splits the two to help you choose the right AI security assessment based on your operational maturity. We’ll also show you how we approach both services.
What Is AI Penetration Testing?
An AI penetration test is a comprehensive search. It asks a simple question:
In how many different ways can we break this system?
The goal of this AI security assessment is environment-specific. We map your entire AI attack surface, looking at your
- Underlying model
- LLM
- Plugins
- Retrieval-augmented generation (RAG) data sources
- Application programming interfaces (APIs) tying it all together
We hunt for every exploitable flaw and test your defences against leading industry frameworks, like MITRE ATLAS and OWASP Top 10. Our team won’t stop when they find a single way in. We document every open door, weak lock, and broken window we find within our testing timeframe.
The output is a comprehensive list of vulnerabilities. You get clear severity ratings and step-by-step reproduction instructions.
Ultimately, you request an AI penetration test to fix your security baseline before an attacker finds the gaps. It's a diagnostic tool that tells you exactly what's broken.
What Is AI Red Teaming?
Red teaming is an objective-based attack. The question it asks is:
Can an adversary successfully accomplish a defined goal without getting caught?
When red teaming, we emulate malicious behaviour. We don’t look for every bug but for a viable path to the agreed target. MITRE states that the AI red teaming practice employs adversarial thinking to counter threats before they happen.
An attacker might want to extract sensitive training data. They might want to bypass your safety filters to generate harmful content. Or, they might want to force your AI agent to execute a malicious command on your internal network.
Our goal is to meet that target while remaining undetected. This tests more than just your code. It tests your monitoring tools, alerting rules, and response team.
The output of an AI red teaming test is a narrative of the attack path. It shows you exactly how an adversary can beat your active defences.
Comparing AI Red Teaming with AI Pentests
Both options involve skilled human experts attacking your AI system. However, their testing methodologies differ significantly for each test.
The Focus of the Assessment
AI penetration testing is loud. Testers send thousands of payloads. They want to trigger errors and see how the system behaves when it fails. They test every input field and every API endpoint.
AI red teaming is quiet. Testers have a specific goal. They evade your logging mechanisms and use slow, multi-turn interactions to manipulate your AI models carefully. They only make noise when it serves a specific objective.
Scope and Boundaries
A penetration test focuses strictly on the AI application. You define a hard boundary around the model and its integrations.
A red team engagement often has a much wider scope. Attackers might use social engineering against your developers. They might look for physical access to a facility. They often attempt lateral movement through your wider corporate network to eventually compromise your AI system.
The Required Operational Maturity
Don’t hire an AI red teaming engagement if you’ve never run a penetration test.
If your application has basic security flaws, the red team will win in five minutes. They’ll achieve their objective using a simple exploit. Such an outcome provides zero value to your business. It just proves that your app is vulnerable, which you already knew.
You need a solid baseline first.
- Run an AI penetration test to find and fix the obvious vulnerabilities.
- Run AI red teaming when you’re confident that your system is secure and you want to test your active detection capabilities.
Expected Outcomes
A pentest provides a ranked list of vulnerabilities based on your business. We map our findings to the OWASP Top 10 for LLM Applications and the OWASP Top 10 for Agent Systems. Your engineering team uses this list to patch the code based on severity.
A red team engagement provides a narrative of the attack path. It exposes flaws in your technical defences and operational responses.
Quick AI Security Assessment Comparison
| Feature | AI Penetration Testing | AI Red Teaming |
| Primary Goal | Find every possible vulnerability (breadth). | Achieve a specific adversarial objective (depth) without getting caught. |
| Testing Approach | Loud and comprehensive; tests all inputs and API endpoints. | Quiet and evasive; avoids triggering security alerts. |
| Target Scope | Limited to the AI application's models, plugins, RAG data sources, and APIs. | The entire defensive posture, including the SOC and human response. |
| Operational Maturity | Low to Medium (building a security baseline). | High (validating active defences). |
| Deliverable | A detailed list of vulnerabilities with severity ratings and resolutions. | An attack narrative detailing the breach path and detection failures. |
| Timeline | Strictly time-boxed (typically a few weeks). | Extended (requires slow, multi-turn manipulation). |
Which AI Security Assessment Do You Need?
Match the service to your current business situation.
Choose AI Penetration Testing if
- You’re launching a new AI feature or product to the public.
- The business recently made major architectural changes to its AI application.
- You must prove system compliance to partners or regulators.
- You’ve never tested your AI implementation before.
Choose AI Red Teaming if
- You already run regular penetration tests on your AI infrastructure.
- A Security Operations Centre (SOC) is monitoring your application.
- You want to know if your team can detect an attack against your AI models.
- Your threat model includes advanced persistent threats or highly motivated adversaries.
Validating Autonomous Agents
Testing an AI chatbot is one thing. Testing an autonomous agent is another.
Agents execute multi-step tasks across APIs and tools. They often have direct system interaction, which introduces new risks. We assess agentic workflows for goal injection, task hijacking, and memory poisoning. We test if an attacker can feed persistent false information into the agent's memory to influence its future decisions.
Automated tools simply can't do this. A scripted scanner can't creatively manipulate a large language model. It can't build custom test harnesses.
Both red teaming and pentesting require skilled, human experts. These experts must understand how to adapt their attacks based on the model's unique, unpredictable responses.
You need a pentest expert to secure modern AI systems. We hold international security certifications and back our work with a 100% quality guarantee. Tell us about your operational maturity, and we’ll scope the service your business needs.
Let’s Secure Your AI
Questions We’re Asked About AI Pentesting and Read Teaming
Does AI red teaming test our security team?
Yes. AI red teaming evaluates your entire defensive posture. It tests your AI models, but it also tests your logging tools, your alerting thresholds, and how quickly your human security team reacts to a live threat.
Can an AI red team target our developers?
Yes, depending on the agreed scope. Red teaming mimics real-world adversaries. The scope can include phishing campaigns against the developers who hold the keys to your AI infrastructure.
Do both services provide a report?
Yes, but the reports look different. An AI penetration test delivers a list of individual vulnerabilities with reproduction steps for your developers. An AI red teaming report delivers a narrative. It details the specific attack path used to achieve the objective and highlights where your detection failed.
How long do these assessments take?
AI penetration tests are strictly time-boxed based on the size of your application. They typically take a few weeks. AI red teaming engagements can take longer. The testers must move slowly to evade your detection mechanisms and map out complex attack chains. We’ll discuss the timeframe for your project once we’ve got the scope pinned down.
Protect Your Artificial Intelligence Models
Don't let a poorly scoped test leave your AI exposed. Speak to our expert team. We’ll review your architecture, discuss your threat model, and help you choose the right approach to protect your assets.
Book Your Free Consultation
Want to Read More?
- Make sure you understand the EU’s AI Act and how it impacts your business and security posture.
- Explore our web application penetration testing services to secure your attack surfaces before integrating complex AI models.
- Prepare your infrastructure for an upcoming assessment by following our LLM pentest checklist.
