AI Pentest – Security Testing for AI Systems & LLM Applications

AI systems introduce unique attack surfaces that traditional security testing does not cover. Prompt injection, jailbreaking, tool-use manipulation via MCP servers, and adversarial attacks require specialized testing methods. We test your AI applications, LLM integrations, agentic AI systems, and ML pipelines for exactly these vulnerabilities – before attackers exploit them.

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When Do You Need an AI Pentest?

Any organization that develops or deploys AI systems creates new attack vectors that are fundamentally different from traditional IT vulnerabilities. An AI pentest is required whenever AI components interact with sensitive data, business-critical decisions, or external users.

LLM-Based Applications & Chatbots: Chatbots, AI assistants, and LLM integrations are vulnerable to prompt injection, jailbreaking, and the unintended disclosure of internal data or system instructions.
AI-Powered Decision Systems: Scoring models, fraud detection, and automated decision processes can be manipulated through adversarial inputs – with direct business consequences.
ML Pipelines & Model Deployments: From training data pipelines to model serving: every stage of the ML infrastructure presents attack surfaces – from data poisoning to model extraction.
Agentic AI & Tool-Use Systems: AI agents with tool access via MCP servers, function calling, or plugins can interact with databases, APIs, and file systems in uncontrolled ways – with potentially critical impact on your entire infrastructure.
EU AI Act & Regulatory Requirements: The EU AI Act requires providers and operators of high-risk AI systems to implement demonstrable security measures. An AI pentest provides the evidence that your systems are robust against manipulation.

AI-Specific Attack Vectors

What We Test

AI systems have vulnerabilities that do not appear in any traditional pentest framework. Our assessments are based on the OWASP Top 10 for LLM Applications and current research in adversarial AI security.

Prompt Injection & Jailbreaking: We test whether attackers can bypass the security boundaries of your LLM through manipulated inputs – both through direct prompts and through indirect injection via embedded documents and data sources.
Training Data Leakage: AI models can disclose confidential information from their training data – personal data, trade secrets, or source code. We systematically test for training data leakage and membership inference.
Adversarial Attacks: Targeted manipulation of input data that causes AI models to produce incorrect results – from image classification and NLP to decision systems. We test the robustness of your models against adversarial examples.
Model Extraction & Supply Chain: We assess whether attackers can reconstruct your model through systematic API queries, and evaluate the security of your AI supply chain – from pre-trained models to third-party plugins.
Agentic AI & Tool-Use Security: We test AI systems with tool access: MCP servers, function calling, and plugin architectures. Key focus areas include indirect prompt injection via tool results, tool poisoning, excessive agency, and data exfiltration through manipulated tool calls.

Illustration of security assessment services

Our Approach

How an AI Pentest Works

An AI pentest requires a different approach than traditional security testing. We combine automated testing tools with manual analysis by AI security experts.

Scoping & Threat Modeling

We analyze your AI architecture: which models are deployed? What data flows in? Who has access to prompts and outputs? Based on this, we create an AI-specific threat model.

Prompt Injection Testing

Systematic testing of all input interfaces for direct and indirect prompt injection. We test whether attackers can extract system instructions, bypass security boundaries, or manipulate model behavior.

Tool-Use & Agent Testing

For systems with tool access, we conduct targeted testing: Can attackers execute indirect prompt injection through manipulated tool results? Can MCP servers or function calls be abused to access unauthorized resources? Can the agent be tricked into performing unintended actions?

Robustness Testing

We test your models with adversarial examples, edge cases, and unexpected inputs. The goal is to assess how robust your AI systems are against targeted manipulation.

Data Leakage Analysis

Targeted attempts to extract confidential training data, system prompts, or internal information through the model. We test for training data leakage, membership inference, and unintended information disclosure.

Infrastructure & API Assessment

Beyond the model itself, we test the surrounding infrastructure: API security, authentication, rate limiting, input validation, and the security of the ML pipeline.

Report & Hardening Recommendations

You receive a detailed report with all identified vulnerabilities, proof-of-concept attacks, and prioritized recommendations for hardening your AI systems.

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Regulatory Context

AI Security Is Becoming Mandatory

The EU AI Act requires providers and operators of high-risk AI systems to implement demonstrable security measures. Robustness against manipulation, transparency, and human oversight are becoming regulatory requirements. At the same time, the OWASP Top 10 for LLM Applications demonstrates that AI-specific vulnerabilities represent real and exploitable risks. An AI pentest provides the evidence that your systems meet these requirements.

OWASP LLM Top 10 & Adversarial ML

Our Testing Methodology

Our AI pentests are based on established frameworks and current research in AI security:

OWASP Top 10 for LLM Applications: Systematic assessment of all ten risk categories: from Prompt Injection (LLM01) and Insecure Output Handling (LLM02) to Model Denial of Service (LLM04) and Sensitive Information Disclosure (LLM06).
Adversarial Machine Learning: Evasion attacks, poisoning attacks, and model stealing – we test your ML models against the attack categories of the NIST Adversarial Machine Learning framework.
Manual Exploitation & Creative Testing: Automated tools alone are not sufficient: our experts conduct creative manual tests – multi-stage jailbreaks, context-dependent prompt injection, and social engineering scenarios targeting AI systems.

Scope of Assessment

What We Assess

We cover the full spectrum of AI systems – from individual chatbot integrations to complex ML infrastructures:

LLM Applications & Chatbots: GPT integrations, custom chatbots, RAG systems, and AI assistants – we test the security of prompt processing, output filtering, and data access controls.
ML Models & Inference APIs: Classification models, recommendation systems, and scoring engines – we test for adversarial robustness, model extraction, and unauthorized data access via the API.
AI Infrastructure & Pipelines: Training data pipelines, model registries, feature stores, and deployment infrastructure – we assess the security of the entire MLOps chain.
Agentic AI & MCP Integrations: AI agents with MCP servers, function calling, and plugin systems – we test the security of tool permissions, validation of tool results, and protection against uncontrolled agent execution.