AI Pentesting
What is AI pentration testing?
AI Penetration Testing is the process of evaluating and securing Artificial Intelligence systems and Machine Learning models against real-world cyberattacks. As AI becomes deeply integrated into business applications—like chatbots, fraud detection, recommendation engines, and automation—the attack surface expands drastically.
AI Penetration Testing simulates adversarial threats to:
- Manipulate training data (data poisoning)
- Exploit models (model inversion or extraction)
- Attack APIs (prompt injection, data leakage)
- Abuse decision logic (adversarial input attacks)
Why AI Pentesting is Essential
- New Attack Surface: Traditional security doesn’t cover ML pipelines or inference APIs.
- Data Sensitivity: AI models are often trained on private data—protecting this is critical.
- Trust & Fairness: Biased or compromised AI decisions can lead to reputational loss and legal issues.
- Compliance & Regulations: AI systems are coming under regulations like the EU AI Act. AI Penetration Testing is crucial for compliance.
- Black-box Behavior: AI systems often act unpredictably—AI Penetration Testing helps uncover hidden vulnerabilities.
Our testing approach
Scoping & Asset discover
we identify the AI model’s architecture and understand how it’s deployed. It determines whether testing will be black-box or white-box. Clearly defining the boundaries helps avoid unintended disruptions.
Reconnaissance
We gather technical details about the AI system it exposes. This includes analyzing and identifying data exposure risks. It sets the foundation for targeted attacks in later steps.
Testing
This step involves crafting inputs to manipulate the model. The goal is to test the model’s robustness and see if it can be tricked. A weak model might misclassify even with small input changes.
Reporting
This phase involves documenting all identified vulnerabilities with clear explanations, severity and impacts with evidence such as screenshots. It provides actionable view of the system’s security posture.
Remediation
We can give remediation and work with the client team to fix the identified issues effectively. Clear recommendations are tailored to the AI system’s architecture and use case.
Re-Testing
A retest is conducted to ensure that all vulnerabilities have been properly addressed with previously reported test cases to ensures that mitigation efforts were successful or not.