AI/ML Penetration Testing
Adversarial security testing of AI systems, large language models, and machine learning pipelines against prompt injection, model theft, data poisoning, and agentic abuse.
Service Overview
AI and machine learning systems introduce a new class of vulnerabilities that traditional application testing does not address. Our AI/ML penetration testing aligns with the OWASP Top 10 for LLM Applications, OWASP Machine Learning Security Top 10, NIST AI RMF, and MITRE ATLAS to evaluate model behavior, training pipelines, inference endpoints, agent frameworks, and MCP integrations. We assess adversarial evasion, prompt injection, model extraction, training-data poisoning, and supply-chain risk across the full ML lifecycle.
$ armour --module ai-ml-pentest
[*] Loading AI/ML Penetration Testing module...
[*] 14 tools available
[!] 6-phase methodology loaded
[+] Ready for engagement
[+] Deliverables: 8 items
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Our Approach
Model & System Reconnaissance
Identify model architectures, hosting endpoints, training data sources, third-party model dependencies, and integrated agent or MCP tooling.
Input Attack Surface Mapping
Enumerate all prompt entry points, retrieval-augmented generation flows, tool-calling boundaries, and trust transitions between user, system, and tool contexts.
Evasion & Poisoning Testing
Craft adversarial inputs, jailbreaks, and indirect prompt injection payloads, and evaluate exposure of training and fine-tuning pipelines to data poisoning.
Model Extraction & Inversion
Test for model theft via query-based extraction, membership inference, and training-data reconstruction against confidentiality of proprietary models.
Supply-Chain Assessment
Review model registries, pretrained weights, datasets, Python dependencies, and inference container images for tampering and known-vulnerable components.
AI Governance Review
Map controls against NIST AI RMF, ISO/IEC 42001, and EU AI Act expectations including logging, human oversight, and abuse monitoring.
Tools & Technologies
Assessment Process
Our structured methodology ensures thorough coverage and actionable results.
Deliverables
- AI/ML threat model and attack surface map
- OWASP LLM Top 10 coverage matrix
- OWASP ML Top 10 findings report
- Prompt injection and jailbreak evidence
- Model extraction and inversion results
- Training and supply-chain risk register
- Guardrail and monitoring recommendations
- Re-test verification after remediation
Industries Served
Key Benefits
LLM-Specific Coverage
Testing aligned to OWASP LLM Top 10 and MITRE ATLAS rather than generic web testing repurposed for AI.
Protect Proprietary Models
Identify model extraction, inversion, and membership-inference exposure before competitors or attackers exploit them.
Agent & MCP Hardening
Validate tool-calling boundaries, MCP server trust, and agentic workflows against prompt-injection-driven abuse.
Data Pipeline Assurance
Surface poisoning and integrity risks across training, fine-tuning, and retrieval data sources.
Regulatory Alignment
Map findings to NIST AI RMF, ISO/IEC 42001, and EU AI Act control expectations for AI risk management.
Actionable Guardrails
Concrete recommendations for input filtering, output validation, rate limiting, and abuse monitoring instead of generic advice.
Frequently Asked Questions
Common questions about our services, methodology, and engagement process.
Ready to Get Started?
Contact our team to discuss your security requirements and receive a customized proposal.