What is prompt injection and why is it dangerous?

Prompt injection is an attack technique where malicious input manipulates an AI system to perform unintended actions — such as leaking confidential data, bypassing security controls or executing commands. It ranks #1 on the OWASP LLM Top 10 because virtually all Large Language Models are vulnerable to it without additional security measures.

Is my AI system vulnerable if I use a commercial LLM?

Yes, even with commercial LLMs like GPT or Claude you are vulnerable to prompt injection, data leakage and insecure output handling. The vulnerabilities exist not only in the model itself, but also in how you integrate it into your application, what data you pass to it and how you process the output.

🤖 AI Security

AI Security Assessment Netherlands

We deliver AI Security in the Netherlands to SME+ organizations.

AI security risks are fundamentally different from traditional application flaws. Prompt injection, data leakage via model output and training data poisoning top the OWASP LLM Top 10 — and no firewall stops them automatically. Our AI security assessments combine automated scanning with manual red teaming based on current attack techniques. You get a concrete action plan, not a thick report that gathers dust.

OWASP LLM Top 10 LLM Pentesting AI Red Teaming Prompt Injection ISO 42001

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LLM Security

LLM Penetration Testing — a new attack surface

An LLM that processes production data, answers client queries or drives internal processes is an attractive target. Our assessments follow the OWASP LLM Top 10 framework.

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Prompt Injection Testing

The most critical LLM vulnerability. Direct injection via the user interface, indirect injection via external data sources the model reads (websites, documents, databases).

System prompt extraction attempts
Testing scope boundary violations

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Data Leakage & Jailbreaking

LLMs with access to sensitive data can disclose it via targeted prompts. We test for training data extraction, jailbreaks, system prompt leakage and RAG data leakage.

PII & trade secret extraction
RAG unauthorised document retrieval

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Model Poisoning & Evasion

Integrity attacks on training pipelines — poisoned data, backdoors, fine-tuning manipulation. We stress test ML training pipelines and inference results.

Training data integrity checks
Backdoor detection
Adversarial robustness testing

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AI Red Team Approach Structured red team exercise: model inventory, threat modelling, exploitation attempts, findings report with risk scores and concrete remediation steps.

OWASP LLM Top 10 coverage
Risk-scored findings report
Remediation guidance included

OWASP LLM Top 10

Complete coverage of the OWASP LLM Top 10 framework

Our assessments cover all ten vulnerability categories from the OWASP LLM Top 10 framework.

LLM01

Prompt Injection

LLM02

Insecure Output Handling

LLM03

Training Data Poisoning

LLM04

Model Denial of Service

LLM05

Supply Chain Vulns

LLM06

Sensitive Info Disclosure

LLM07

Insecure Plugin Design

LLM08

Excessive Agency

LLM09

Overreliance on AI

LLM10

Model Theft & Extraction

AI Governance & ISO 42001

Waarom AI Governance nu essentieel is

AI-systemen nemen steeds vaker beslissingen die mensen raken. Zonder governance lopen organizations risico op discriminerende uitkomsten, reputatieschade, juridische liability en non-compliance with the EU AI Act.

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ISO 42001:2023 — AI Managementsysteem

ISO/IEC 42001:2023 is de internationale norm for opzetten en beheren van een AI Managementsysteem (AIMS). Het biedt een gestructureerd kader voor verantwoord AI-gebruik, governance en risicobeheer. Sluit direct aan op ISO 27001.

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EU AI Act Compliance

The EU AI Act categorises protecting AI systems for your business by risk level. High-risk systems require conformity assessments, human oversight and robust documentation. We implement the required measures.

AI Governance Services

AI Management System (AIMS) setup
AI risk and impact assessments
EU AI Act gap analysis and compliance
AI policy development
AI transparency & explainability
NIST AI RMF implementation
AI Ethics framework

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