AI red teaming and traditional penetration testing share a goal – find security weaknesses before attackers do – but they target different systems, use different techniques, and require different skills. Traditional pentesting focuses on applications, networks, and infrastructure with deterministic behaviour. AI red teaming targets machine learning models, training pipelines, and AI-powered applications where the system’s behaviour is non-deterministic and the attack surface includes the model itself. This guide explains how they differ, where they overlap, and when you need each.
Table of contents
Comparison Table
| AI Red Teaming | Traditional Pentesting | |
|---|---|---|
| Primary target | ML models, training data, AI pipelines | Applications, networks, infrastructure |
| Input type | Natural language prompts, adversarial examples | Structured parameters, payloads |
| System behaviour | Non-deterministic (same input, different outputs) | Deterministic (same input, same output) |
| Attack classes | Prompt injection, data poisoning, model inversion, evasion etc | SQLi, XSS, SSRF, auth bypass, privilege escalation etc |
| Standards | OWASP LLM Top 10, NIST AI RMF, EU AI Act | OWASP Top 10, PTES, OSSTMM, CREST |
| Tools | Promptfoo, Garak, PyRIT, custom prompt libraries | Burp Suite, OWASP ZAP, Nmap, Metasploit |
| Skills needed | ML/NLP understanding + security expertise | Network/app security expertise |
| Output format | Behavioural findings, guardrail bypass evidence, model vulnerabilities | Technical vulnerabilities, exploit evidence, remediation steps |
| Frequency | Before deployment + after model/prompt changes | Annual + after significant infrastructure changes |
What Is AI Red Teaming?
AI red teaming is a structured adversarial exercise that probes AI systems for weaknesses. It goes beyond traditional security testing to include risks specific to machine learning: prompt injection, model manipulation, training data extraction, bias exploitation, safety guardrail bypass, and abuse of tool-calling capabilities.
The term “red teaming” in the AI context has broader scope than in traditional cybersecurity. It covers not just security vulnerabilities but also safety risks (harmful content generation), reliability risks (hallucinations, inconsistent behaviour), and trust risks (bias, fairness, transparency). A security-focused AI red team concentrates on the vulnerability and exploitation aspects, while safety-focused red teams address content policy and societal harms.
Our LLM security testing focuses on the security side: can an attacker extract data, bypass access controls, trigger unauthorised actions, or compromise the system through the AI component?
What Is Traditional Penetration Testing?
Traditional penetration testing assesses the security of applications, networks, and infrastructure by simulating real attacks. Testers look for vulnerabilities in web applications (injection, broken authentication, access control), APIs (authorisation, data exposure), networks (misconfigurations, unpatched services), and mobile apps (insecure storage, certificate pinning).
The target systems are deterministic – the same request produces the same response. The attacks are well-categorised (OWASP Top 10, CWE). The testing methodology is mature and standardised. A CREST-accredited pentest follows an established framework with predictable coverage.
Key Differences
Determinism. The most fundamental difference. In traditional pentesting, if an SQL injection works once, it works every time. In AI red teaming, a prompt injection that succeeds on one attempt may fail on the next because the model’s response varies. This requires statistical validation – running attacks multiple times and measuring success rates rather than recording a single pass/fail.
Attack surface. Traditional attacks target code and configuration. AI attacks also target the model’s learned behaviour, the training data, the retrieval pipeline, and the natural language instructions that define business logic. The system prompt is both a security control and an attack target.
Threat modelling. Traditional threat models focus on authentication, authorisation, and data flow. AI threat models must also consider model supply chain risks, data poisoning vectors, inference-time attacks, and the trust boundary between the model and the tools it can call.
Skill set. Traditional pentesters need deep knowledge of web protocols, operating systems, and network stacks. AI red teamers additionally need understanding of how LLMs work – tokenisation, attention, context windows, fine-tuning, embedding spaces, and retrieval augmentation. The best AI security testers have both skill sets.
Unique AI Attack Surfaces
AI red teaming covers attack classes that do not exist in traditional pentesting:
- Model manipulation – Causing the model to produce outputs it was designed to refuse, through adversarial prompting, encoding tricks, or multi-step conversation manipulation.
- Data poisoning – Injecting malicious content into training data, fine-tuning datasets, or RAG knowledge bases to influence model behaviour at inference time.
- Model inversion and extraction – Extracting training data or model parameters through targeted queries. This can reveal PII, proprietary data, or enable model theft.
- Evasion attacks – Crafting inputs that cause the model to misclassify or misinterpret, relevant for models used in content moderation, fraud detection, or security monitoring.
- Embedding and vector attacks – Manipulating how content is represented in vector space to influence retrieval rankings, poison search results, or bypass access controls in RAG pipelines.
Regulatory Context
The EU AI Act introduces requirements for testing high-risk AI systems that go beyond traditional security assessments. Article 9 requires risk management throughout the AI system lifecycle, including adversarial testing. The Act does not prescribe specific testing methodologies, but AI red teaming is the most direct way to demonstrate that adversarial risks have been evaluated.
In the UK, the AI Safety Institute conducts red teaming of frontier models. For organisations deploying AI systems, the combination of LLM-specific pentesting (for security vulnerabilities) and traditional pentesting (for the application layer) provides the most complete risk assessment.
When You Need Which
- Web app or API with no AI component – Traditional pentesting only.
- LLM-powered chatbot or agent – Both. AI red teaming for the model layer, traditional pentesting for the application layer.
- RAG-based knowledge system – Both, with specific focus on the retrieval pipeline and document access controls.
- ML model in production (classification, fraud detection) – AI red teaming focused on evasion and data poisoning, plus traditional infrastructure testing.
- EU AI Act compliance for high-risk systems – AI red teaming is the most direct path to demonstrating adversarial risk assessment.
FORTBRIDGE is a CREST-accredited penetration testing company with dedicated LLM security testing and traditional pentesting services. We cover both the AI and application layers in a single engagement. Get in touch to discuss your requirements.
Frequently Asked Questions
What is AI red teaming?
AI red teaming is an adversarial exercise that tests AI systems for security vulnerabilities, safety risks, and reliability issues. It targets the model itself, its training data, retrieval pipelines, and tool-calling capabilities using techniques like prompt injection, data poisoning, and model manipulation.
How does AI red teaming differ from traditional pentesting?
Traditional pentesting targets deterministic systems (applications, networks) with structured attacks (SQLi, XSS). AI red teaming targets non-deterministic models with natural language attacks (prompt injection, jailbreaking). Different tools, skills, and standards apply. Most LLM applications need both.
Does the EU AI Act require AI red teaming?
The EU AI Act requires risk management and adversarial testing for high-risk AI systems (Article 9) but does not prescribe specific methodologies. AI red teaming is the most direct way to demonstrate compliance with the adversarial testing requirement.
Can traditional pentesters do AI red teaming?
They need additional skills. AI red teaming requires understanding of LLM internals (tokenisation, attention, context windows), adversarial ML techniques, and prompt engineering. The best AI security testers combine traditional pentesting expertise with ML knowledge.
Do I need a separate AI red team engagement?
Not necessarily. A combined engagement that covers both the AI and application layers is often more efficient. The traditional pentest covers authentication, authorisation, and API security. The AI red team component covers prompt injection, data leakage, and model-specific attacks. We deliver both in a single assessment.
