AI Security
Testing
In-depth security assessment of AI/ML systems, large language models, and generative AI applications to identify unique vulnerabilities in modern AI deployments
Our AI Security Approach
KoreLogic's AI security testing methodology addresses the full AI stack - from data sources and model infrastructure through agents, APIs, and integration points - guided by CSA, OWASP, MITRE ATLAS, NIST, and BIML frameworks.
LLM Vulnerability Assessment
Thorough testing against the OWASP LLM Top 10 including prompt injection, insecure output handling, training data poisoning, and model denial of service attacks.
Adversarial AI Testing
Advanced adversarial attacks including evasion attacks, model extraction, membership inference, and robustness testing of AI decision systems.
Full-Stack AI Infrastructure
End-to-end assessment across the AI stack: data and data sources, model serving platforms, training environments, AI-augmented applications and APIs, and AI integration points within the broader enterprise architecture.
Agentic AI Assessment
Security evaluation of autonomous AI agents and multi-agent workflows - agent sandboxing, constraint enforcement, action provenance, MCP protocol security, prompt and context integrity, and memory mode isolation.
AI Stack Coverage
AI Security Testing Services
Four core service lines covering the full AI security landscape - from architecture review through hands-on penetration testing of agentic systems and vendor-developed AI products.
AI Security Architecture Review
Comprehensive review of your AI deployment architecture across the full stack - data sources, models, agents, APIs, and integration points - to identify systemic security risks before they become vulnerabilities.
- - Full-stack AI architecture assessment
- - Data pipeline and model supply chain review
- - AI integration point analysis
- - Governance and compliance posture evaluation
Penetration Testing of AI-Augmented Applications
Hands-on penetration testing of web applications, APIs, and services that incorporate LLMs, chatbots, or other AI components - tested against OWASP LLM Top 10 and traditional web application attack vectors.
- - Prompt injection and jailbreaking
- - Insecure output handling and data leakage
- - LLM-specific attack chains
- - Traditional web app vectors at AI integration points
Agentic AI Penetration Testing
Security testing of autonomous AI agents and multi-agent systems using CSA AI Red Teaming guidance - evaluating sandboxing, constraint enforcement, tool use security, and action provenance across agent architectures.
- - Agent sandboxing and isolation testing
- - MCP protocol and tool integration security
- - Code and action provenance verification
- - Multi-agent trust boundary validation
Product Security of Vendor AI Solutions
Independent security evaluation of vendor-developed AI products and platforms your organization is considering or already deploying - assessing model robustness, data handling, and supply chain integrity.
- - Vendor AI product security assessment
- - Model robustness and adversarial testing
- - AI supply chain and third-party risk
- - Privacy, bias, and compliance evaluation
Supporting Capabilities
Specialized expertise that underpins each of our four core service lines - from adversarial model testing and data pipeline security through governance and regulatory compliance.
ML Model Security
Advanced adversarial testing of machine learning models and AI decision systems to evaluate robustness against targeted attacks.
- - Adversarial example generation
- - Model extraction and theft attacks
- - Membership inference testing
- - Evasion and robustness assessment
Training Data Security
Assessment of training data integrity, privacy protection, and pipeline security to prevent data poisoning and unauthorized exposure.
- - Data poisoning detection
- - Privacy leakage assessment
- - Data provenance verification
- - Sensitive data exposure analysis
- - Training pipeline integrity
AI Governance & Compliance
Evaluation of AI governance posture and regulatory readiness aligned with established risk management frameworks.
- - NIST AI RMF alignment
- - BIML framework assessment
- - Regulatory compliance evaluation
- - Bias and fairness testing
- - Explainability assessment
Assessment Deliverables
Executive Summary
High-level overview of AI security risks, business impact, and strategic recommendations
Technical Findings
Detailed analysis of AI vulnerabilities, attack vectors, and proof-of-concept demonstrations
AI Security Roadmap
Strategic roadmap with prioritized recommendations
Ongoing Support
Post-assessment remediation guidance and follow-up testing recommendations
Professional Reports
Detailed AI security assessment deliverables that provide actionable insights and strategic recommendations.
Ready to Strengthen Your Security?
Our AI security experts will help you identify and mitigate risks across your AI systems, models, and agentic workflows.
Confidential consultation — Expert recommendations — Detailed reporting