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 Service 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
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
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