Cutting-Edge AI Security

AI & LLMPenetration Testing

Specialized security testing for AI systems, machine learning models, and Large Language Model implementations. Identify vulnerabilities in prompt injection, model poisoning, data extraction, and algorithmic bias before they impact your organization.

AI Security Testing Areas

Comprehensive security assessment covering all aspects of AI and machine learning systems

Model Security Assessment

Comprehensive evaluation of AI model vulnerabilities and attack vectors

Model architecture analysis
Training data poisoning detection
Adversarial example generation
Model extraction attacks
Backdoor detection
Model inversion attacks

Prompt Injection Testing

Advanced testing for prompt injection vulnerabilities in LLM applications

Direct prompt injection
Indirect prompt injection
Jailbreaking techniques
System prompt extraction
Context manipulation
Multi-turn conversation attacks

Data Privacy & Extraction

Assessment of data leakage and privacy vulnerabilities in AI systems

Training data extraction
Membership inference attacks
PII leakage detection
Model memorization testing
Data reconstruction attacks
Privacy-preserving mechanisms evaluation

AI Bias & Fairness Testing

Evaluation of algorithmic bias and fairness in AI decision-making systems

Demographic bias assessment
Fairness metric evaluation
Discriminatory output detection
Protected attribute analysis
Intersectional bias testing
Bias mitigation validation

AI Infrastructure Security

Security assessment of AI/ML infrastructure and deployment environments

MLOps pipeline security
Model serving infrastructure
API security assessment
Container and orchestration security
Cloud AI service configuration
Model versioning and access controls

AI Application Security

Security testing of applications that integrate AI and LLM capabilities

AI-powered application testing
Integration point vulnerabilities
Authentication and authorization
Input validation and sanitization
Output filtering mechanisms
Rate limiting and abuse prevention

AI Threat Landscape

Understanding the evolving threat landscape targeting AI and machine learning systems

Model Attacks

Adversarial examples
Model poisoning
Backdoor attacks
Model extraction
Evasion attacks

Data Attacks

Training data poisoning
Data extraction
Membership inference
Property inference
Model inversion

Prompt Attacks

Prompt injection
Jailbreaking
Context manipulation
System prompt extraction
Multi-turn exploitation

Infrastructure Attacks

MLOps pipeline compromise
Model serving attacks
API vulnerabilities
Container escapes
Supply chain attacks

Our AI Security Methodology

A structured approach to AI security testing based on industry best practices and cutting-edge research

AI System Discovery

Comprehensive mapping of AI components, models, and data flows

AI asset inventory
Model identification
Data pipeline mapping
Integration point identification

Threat Modeling

AI-specific threat modeling and attack surface analysis

AI threat landscape analysis
Attack vector identification
Risk assessment
Test case development

Security Testing

Hands-on testing using specialized AI security tools and techniques

Automated vulnerability scanning
Manual penetration testing
Adversarial testing
Bias and fairness evaluation

Analysis & Reporting

Comprehensive analysis and actionable recommendations

Vulnerability analysis
Risk prioritization
Remediation guidance
Executive reporting

Specialized AI Security Tools

We leverage cutting-edge tools and frameworks specifically designed for AI security testing

Adversarial Robustness Toolbox (ART)

CleverHans

Foolbox

TextAttack

PromptInject

AI Red Team Toolkit

Fairness Indicators

What-If Tool

Custom AI Security Tools

Assessment Deliverables

Comprehensive reporting and actionable recommendations for your AI security posture

Executive Summary Report

High-level overview of findings and business impact assessment

Technical Vulnerability Report

Detailed technical findings with proof-of-concept demonstrations

AI Security Recommendations

Prioritized remediation guidance and security best practices

Bias and Fairness Assessment

Comprehensive evaluation of algorithmic bias and fairness metrics

Remediation Roadmap

Step-by-step plan for addressing identified vulnerabilities

Why AI Security Testing Matters

Growing Attack Surface

AI systems introduce new attack vectors that traditional security testing doesn't cover

Data Privacy Risks

AI models can inadvertently expose sensitive training data or personal information

Bias & Fairness

Algorithmic bias can lead to discriminatory outcomes and regulatory compliance issues

Secure Your AI Future

Don't let AI vulnerabilities compromise your organization's security and reputation. Contact our AI security experts today to schedule a comprehensive assessment of your AI systems.