AI is changing the attack surface faster than many organisations can keep up with. Where classic pentests mainly focus on known vulnerabilities in web apps and infrastructure, we’re now seeing a new category of risk: advanced exploits that abuse AI workflows, model behaviour and automated decisions.
For security teams, the question is no longer whether AI risks are relevant, but how you demonstrably control them before they lead to incidents.
What do we mean by advanced AI exploits?
Advanced exploits are attack techniques that combine multiple weak points. Not one bug, but a chain. With AI systems in particular, this often works surprisingly well, because models, data, tools and permissions all converge into a single process.
Examples we regularly assess in practice:
- Multi-step prompt injection: an attacker manipulates prompts across multiple steps to bypass guardrails.
- RAG poisoning: poisoned sources steer model outputs towards incorrect or harmful actions.
- Privilege pivoting via AI agents: the model indirectly gains access to systems outside its intended scope.
- Indirect data exfiltration: sensitive data leaks via seemingly normal output or debug channels.
- Toolchain abuse: AI-triggered APIs or scripts are abused for unauthorised transactions.
Why traditional security can fall short here
Many security measures are designed for predictable software logic. AI systems behave more dynamically. As a result, a system can be technically “correctly” configured, yet still turn out to be operationally unsafe due to unexpected interactions between prompts, context and actions.
That’s why a combined approach is needed: classic pentest methodology plus AI-specific attack simulation.
AI security testing approach: from attack chain to concrete fixes
- Threat modeling of the AI chain — where are the most likely and most impactful scenarios?
- Adversarial testing — controlled simulations at the prompt, data and action layers.
- Exploit chaining — testing combinations (e.g. prompt injection + overly broad API permissions).
- Impact validation — what’s the real business impact: data breach, fraud risk, downtime or compliance damage?
- Prioritised remediation — quickly executable measures with maximum risk reduction.
5 measures that immediately remove a lot of risk
- Least privilege for AI tools: never give agents more rights than strictly necessary.
- Input/output policy enforcement: explicitly validate prompts, context and critical outputs.
- Source validation in RAG: only allow trusted and classified content.
- Transaction approval gates: sensitive actions require additional authorization outside the model.
- Continuous monitoring: detect abnormal model behaviour and suspicious request patterns early.
What does this deliver for customers and the business?
- lower chance of data breaches and reputational damage,
- faster and safer AI innovation,
- demonstrable security towards customers and auditors,
- higher reliability of AI functionality in production.
AI pentesting in Hoofddorp, Amsterdam and internationally
MonkeysICT supports organisations in the Hoofddorp/Amsterdam region and beyond with advanced pentests on web applications, APIs, internal infrastructure and AI-driven environments. The result isn’t just a list of findings, but a clear plan to actually reduce risk.
Want to know how vulnerable your AI environment is to advanced exploits? Request a quote or see our services for API pentest, web application pentest and external pentest.
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