What is AI Penetration Testing
AI Penetration Testing focuses on identifying vulnerabilities in AI-powered systems such as machine learning models, large language models (LLMs), AI applications, and chatbots by simulating real-world attacker behavior. Security experts perform controlled adversarial attacks to uncover risks like unauthorized access, data leakage, model manipulation, and system disruption. Unlike traditional security testing, AI Pentesting addresses unique threats such as prompt injection, adversarial inputs, model theft, and data poisoning, which can severely impact decision-making, accuracy, and system integrity. With AI increasingly handling sensitive data and business-critical operations, regular testing ensures safer deployments, maintains trustworthy performance, and supports compliance with emerging standards like ISO/IEC 42001 for secure AI management.
AI Penetration Testing Methodology
Planning & Scoping
Cyberous begins by defining the AI system’s scope, including model architecture, data sources, APIs, and deployment environment. We identify sensitive components, attack surfaces, and compliance requirements to design a focused and safe AI Pentesting plan.
Information Gathering
In this phase, we collect detailed insights about training data, model workflows, feature engineering, API endpoints, authentication methods, and system integrations. This helps map the entire AI pipeline and understand where vulnerabilities may exist.
Threat Modeling
Cyberous analyzes potential threats specific to AI systems, such as prompt injection, adversarial inputs, data poisoning, model theft, inference manipulation, and bias exploitation. We create realistic attack scenarios tailored to the model type and use case.
Vulnerability Analysis
Our experts examine the model’s behavior, data flow, and API interactions to identify weak points. We test for insecure configurations, weak access controls, data exposure, unsafe model responses, and logic flaws using automated and manual techniques.
Exploitation Phase
Cyberous performs controlled adversarial attacks to validate the real-world impact of vulnerabilities. This includes prompt injections, model evasion attempts, adversarial examples, API manipulation, and data poisoning simulations to measure system resilience.
Post-Exploitation Analysis
We evaluate how far an attacker could go after exploitation—such as gaining sensitive outputs, altering model decisions, extracting training data, or manipulating system behavior. This phase highlights long-term and large-scale risks.
Reporting & Remediation Guidance
Cyberous delivers a detailed AI security report with vulnerability severity, attack paths, model impact, and actionable remediation steps, including AI-hardening practices and safe model development guidelines. We support teams with fixes aligned to ISO/IEC 42001.
Frequently Asked Questions
AI Penetration Testing evaluates machine learning models, LLMs, AI applications, and data pipelines to identify vulnerabilities specific to AI-driven systems.
Traditional testing checks code and infrastructure, while AI Pentesting focuses on risks like prompt injection, adversarial attacks, data poisoning, model evasion, and model theft.
Cyberous tests ML models, LLMs, AI chatbots, recommendation engines, predictive models, computer vision systems, and AI-powered APIs.
Yes. Attackers can craft adversarial inputs or prompt injections to alter model decisions, extract sensitive data, or bypass restrictions.
Yes. Cyberous checks for data poisoning risks, insecure datasets, weak preprocessing pipelines, and vulnerabilities in data sourcing.
AI systems should be tested before deployment, after major training updates, and periodically as part of ongoing AI security maintenance.
Yes. Cyberous provides detailed remediation steps, AI-hardening practices, and safe model development recommendations aligned with ISO/IEC 42001.