AI/ML Pentest
AI-powered systems—including machine learning models, LLM-driven features, and automation pipelines—introduce new security risks that traditional penetration testing does not cover. Attackers can exploit model weaknesses, manipulate training data, abuse prompt injection, or bypass safety controls to produce harmful outputs, leak sensitive information, or subvert business logic.
Our AI/ML Penetration Testing evaluates the security, reliability, and trustworthiness of your AI models, data pipelines, and deployed AI applications. We simulate real-world adversarial threats to identify vulnerabilities across model design, training, inference, API integration, and prompt interaction.
Our Pentest Methodology
1
Scoping & Planning
We define the assessment goals, identify model types (LLMs, ML models, classifiers, agents), review data flows and integration points, and outline how AI/ML components interact with your applications and APIs. This ensures a safe, structured, and clearly defined engagement.
2
Reconnaissance & Information Gathering
We analyze prompts, training data sources, model inputs/outputs, API endpoints, guardrails, and backend workflows to understand how the model behaves and where attackers may influence or manipulate it.
3
Threat Modeling & Attack Surface Analysis
We map high-risk areas—including user interactions, prompt structures, model decisions, data exposure points, and integration risks—to prioritize likely attack scenarios and evaluate business impact.
4
Vulnerability Discovery & Exploitation
We perform targeted testing for prompt injection, jailbreaks, data leakage, model manipulation, adversarial inputs, unsafe output generation, and API abuse using controlled, safe techniques tailored to your model architecture.
5
Post-Exploitation & Risk Assessment
We assess the real-world impact of successful attacks, covering scenarios such as unauthorized actions, model extraction, data reconstruction, harmful outputs, or bypassed safety controls—all while maintaining strict testing boundaries.
6
Reporting & Remediation Support
You receive a comprehensive report including an executive summary, detailed findings, sample prompts/queries, PoC evidence, prioritized remediation guidance, and mapping to AI security frameworks. A complimentary retest is included to verify fixes.
What We Test
Our testing covers all critical areas of AI/ML security
AI Models
Large Language Models (LLMs), Vision models (object detection, OCR), Speech recognition & NLP models, Recommendation engines, Predictive analytics models, Custom ML & deep learning models
MLOps & AI Infrastructure
Data pipelines, Model training pipelines, Containerized deployment, Serverless inference, CI/CD workflows, Feature stores, Model registries, AI agents & tool integrations
Security Areas Covered
Prompt injection, Model extraction, Data poisoning, Model inversion attacks, API abuse, Logic manipulation, Data leakage, Safety bypass, Adversarial inputs, Cloud misconfigurations, Model supply-chain risks
Compliance & Standards
Our assessment helps with:
Compliance Support
GDPR
protection of personal data in ML pipelines
HIPAA
PHI protection in AI workflows
ISO 27001
secure development & data protection
PCI DSS
card data protection in AI systems
SOC 2
controls around availability & confidentiality
EU AI Act
risk classification & safety
Testing Standards
OWASP Top 10 for LLM Applications
NIST AI Risk Management Framework
OWASP ML/AI Security Guidelines
NIST SP 800-53 & NIST 800-218 (SSDF)
MITRE ATLAS
(Adversarial ML knowledge base)
CIS Benchmarks
(cloud-hosted AI environments)
Google/AWS/Azure AI Security Best Practices
Frequently Asked Questions
Common questions about our web application penetration testing services
No. AI systems require specialized testing for prompt injection, model extraction, data poisoning, and adversarial inputs that traditional tests don’t cover.
We can test black-box, grey-box, or white-box, depending on your preference.
Yes—whether built on open-source models or commercial APIs.
Absolutely. Controlled, safe, and ethical jailbreak attempts are a core part of our methodology.
No. We use controlled test accounts, rate-limited queries, and safe payloads.
Yes. We include detailed fixes, guardrails, filtering strategies, and safe prompting patterns.
Retesting is available upon request and depends on the scope of remediation. We recommend connecting with our team to understand the best retesting approach for your environment.
Typically 2–4 weeks, depending on model complexity and integrations.
READY TO SECURE YOUR AI SYSTEMS?
Schedule a consultation to discuss your AI model, risks, and receive a customized AI/ML Penetration Testing proposal.