Artificial intelligence penetration testing services

Identify vulnerabilities, misuse paths and security gaps in AI, ML and LLM-enabled systems with structured testing and risk-based guidance from TÜV SÜD.

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What is artificial intelligence penetration testing?

Artificial intelligence penetration testing is a specialised security assessment for AI, ML and LLM-enabled systems. It is designed to identify vulnerabilities, misuse paths and weaknesses that may not be covered by traditional application or infrastructure testing.

Unlike conventional penetration testing, it examines risks across the wider AI system, including model behaviour, prompts, APIs, data flows, integrations and access controls. This helps organisations detect issues such as prompt injection, data leakage, model misuse, unauthorised access and other AI-specific attack paths before they affect business operations.

TÜV SÜD provides structured artificial intelligence penetration testing to help organisations strengthen security, reduce risk and support more trustworthy AI deployment.

Why AI systems need specialised security testing

AI introduces new attack paths
AI, ML and LLM systems can face risks such as prompt injection, model misuse and adversarial manipulation that traditional testing may not fully address.
Security issues can affect trust and operations
Weaknesses in AI systems can lead to data leakage, unreliable outputs, unauthorised access or misuse, affecting security, resilience and business trust.
Regulated environments need stronger assurance
For organisations operating in regulated or high-consequence sectors, AI security weaknesses can also create governance, compliance and operational risks.

How TÜV SÜD supports secure AI deployment

TÜV SÜD helps organisations identify and address security weaknesses in AI, ML and LLM-enabled systems before they become larger security, operational or trust-related issues. Our approach combines AI penetration testing with structured risk evaluation, helping teams understand where their systems are exposed, how those weaknesses could be exploited and what actions to prioritise for more secure deployment.

Identify relevant AI security weaknesses

TÜV SÜD looks beyond conventional application testing to assess how AI systems may be exposed across models, prompts, interfaces, data flows and supporting controls. This helps organisations uncover weaknesses that may otherwise remain hidden until they affect system behaviour, sensitive information or user trust in production environments.

Prioritise action based on risk and context

Not every finding carries the same operational impact. TÜV SÜD helps organisations understand which weaknesses matter most based on the AI system’s purpose, deployment context, level of exposure and the sensitivity of the data or decisions involved. This supports more focused remediation and better use of internal resources.

Support secure AI deployment with a structured approach

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AI security needs to be addressed in a way that is practical for real production environments. TÜV SÜD provides a structured assessment approach that helps organisations strengthen security, improve resilience and support governance expectations where AI systems are used in business-critical or regulated settings

Discuss your AI penetration testing needs

Speak with TÜV SÜD about your AI applications, deployment environment and assessment scope to determine the right next steps.

What our AI penetration testing services include

TÜV SÜD provides specialised artificial intelligence penetration testing services for AI, ML and LLM-enabled systems across the model lifecycle. The service helps organisations identify vulnerabilities, misconfigurations and abuse paths across models, data pipelines, APIs, integrations and access controls, so they can better understand security exposure in production environments.

LLM and generative AI security testing

Security testing for enterprise LLM applications, chatbots, APIs and AI-enabled platforms that rely on large language models.

Key focus areas include:

prompt injection and prompt leakage
output manipulation and misuse scenarios
unauthorised access to LLM-enabled functions
weaknesses that may affect security, trust and operational reliability

AI and machine learning model assessment

Assessment of predictive and custom AI models used in business applications and automated decision processes.

This includes review of:

model architecture
inference behaviour
adversarial exposure
risks related to model inversion and model extraction

This helps identify weaknesses that may compromise sensitive data, intellectual property or model integrity.

Data pipeline and training security review

Review of the processes that support model development and operation, including data ingestion, preprocessing and training-related workflows.

Areas assessed include:

data ingestion and preprocessing
training dataset integrity and provenance
weaknesses that may affect model robustness
risks to the overall trustworthiness of the AI system

AI API, integration and access control testing

Testing of the interfaces and supporting environments connected to the AI system.

This includes:

AI APIs
application integrations
connected components
access controls

The assessment looks for insecure interfaces, abuse opportunities, unauthorised model access and weaknesses that could expose sensitive data or disrupt system integrity.

Adversarial simulation and risk evaluation

Simulation of realistic threat scenarios and AI-specific attack techniques to test how the system performs under hostile conditions.

Outputs include:

documented findings
clear risk prioritisation
practical remediation guidance

This supports stronger confidentiality, integrity, availability, reliability and fairness across AI-enabled operations.

How an AI system penetration test is carried out

TÜV SÜD provides structured AI penetration testing tailored to the system, context and use, focusing on security and resilience.

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Define scope

Review of available technical documentation, model architecture, training processes, API integrations and system dependencies to understand the AI system’s operating context and define the assessment scope.

Get started with TÜV SÜD

Identify vulnerabilities, misuse paths and security gaps in AI, ML and LLM-enabled systems with structured testing from TÜV SÜD.

Frequently asked questions (FAQs)

What is AI penetration testing?

AI penetration testing is a specialised security assessment designed to identify vulnerabilities, misconfigurations and abuse paths in AI, ML and LLM-enabled systems. It examines risks across models, data pipelines, APIs, integrations and access controls, not just conventional applications or infrastructure.

How is AI penetration testing different from traditional penetration testing?

Traditional penetration testing focuses mainly on applications, networks and infrastructure. AI penetration testing also assesses model behaviour, prompts, training-related processes, inference logic and AI-specific attack techniques such as prompt injection, model inversion, model extraction and data poisoning.

What security risks can AI penetration testing help identify?

AI penetration testing can help identify risks such as prompt injection, prompt leakage, adversarial input manipulation, model inversion, model extraction, training data poisoning, unauthorised model access and AI API abuse. These risks can affect sensitive data, system integrity, reliability and trust.

Why is prompt injection a major AI security concern?

Prompt injection can manipulate a model’s behaviour through crafted inputs, potentially bypassing intended safeguards and causing unsafe outputs, sensitive information exposure or misuse of connected functions. OWASP treats prompt injection as a core LLM security risk, which is why it is often a key focus in AI security testing.

What types of AI systems can be assessed?

The service can be applied to a range of deployment scenarios, including LLM applications, enterprise chatbots, AI-enabled platforms, predictive machine learning models and custom AI systems used in business processes or automated decision-making.

What parts of the AI system are included in the assessment?

Depending on scope, the assessment may cover model architecture and inference mechanisms, training and data ingestion pipelines, AI APIs, integrations, application interfaces, governance measures and access controls.

How can I find out whether AI penetration testing is right for my system?

The suitability and scope of AI penetration testing depend on the type of AI system, its deployment context and the risks involved. TÜV SÜD can help evaluate your use case and define an assessment scope aligned with your AI applications, interfaces and supporting environments.

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