### When Your Systems Learn, So Do The Threats
AI and machine learning systems introduce unique security challenges. Traditional security testing can't find
adversarial inputs that fool models, poisoned training data, or biased decision-making. As organizations deploy
AI in critical applications—from fraud detection to autonomous systems—ML security becomes mission-critical.
### AI/ML-Specific Threats
**Model Attacks**
- Adversarial examples (inputs designed to fool the model)
- Model inversion (extract training data from the model)
- Model extraction (steal the model by querying it)
- Membership inference (determine if data was in training set)
**Training Pipeline Attacks**
- Data poisoning (malicious training data)
- Backdoor attacks (trigger words that change behavior)
- Label manipulation
- Supply chain attacks on pre-trained models
**Deployment Attacks**
- Prompt injection (for LLMs and generative AI)
- Adversarial perturbations in production
- Model theft through API access
- Concept drift exploitation
**Privacy Risks**
- Training data memorization
- PII leakage through model outputs
- Differential privacy violations
- Re-identification attacks
### What We Test
**Model Robustness**
- Adversarial robustness testing (FGSM, PGD, C&W attacks)
- Input perturbation tolerance
- Out-of-distribution (OOD) detection
- Confidence calibration
- Edge case and corner case discovery
**Training Data Security**
- Data poisoning resilience
- Backdoor detection
- Label integrity verification
- Data quality and bias assessment
- Supply chain validation (pre-trained models)
**Model Privacy**
- Training data extraction attempts
- Membership inference attacks
- Model inversion testing
- Differential privacy validation
- PII leakage assessment
**Deployment Security**
- API rate limiting and abuse prevention
- Model stealing attempts through API
- Prompt injection testing (LLMs)
- Input validation and sanitization
- Model versioning and rollback capability
**Explainability & Fairness**
- Decision transparency and explainability
- Bias detection across demographics
- Fairness metric evaluation
- Regulatory compliance (EU AI Act, etc.)
- Audit trail and logging
### Our Testing Methodology
**Phase 1: Model Architecture Review**
- Threat modeling for ML system
- Attack surface identification
- Privacy risk assessment
- Compliance review
**Phase 2: Adversarial Robustness Testing**
- White-box attacks (with model access)
- Black-box attacks (query-based)
- Transferability analysis
- Defense evaluation
**Phase 3: Training Pipeline Analysis**
- Data poisoning attempts
- Backdoor detection
- Supply chain validation
- Data integrity verification
**Phase 4: Deployment Security Testing**
- API security assessment
- Model extraction attempts
- Prompt injection (if applicable)
- Runtime monitoring evaluation
**Phase 5: Privacy & Compliance**
- Data leakage testing
- Privacy mechanism validation
- Bias and fairness evaluation
- Regulatory compliance mapping
### ML Systems We Secure
**Computer Vision**
- Image classification and object detection
- Facial recognition systems
- Autonomous vehicle perception
- Medical imaging diagnostics
**Natural Language Processing**
- Large Language Models (LLMs)
- Sentiment analysis and content moderation
- Machine translation
- Chatbots and conversational AI
**Recommender Systems**
- E-commerce and content recommendations
- Financial product suggestions
- Healthcare treatment recommendations
**Anomaly Detection**
- Fraud detection systems
- Intrusion detection
- Quality control automation
- Predictive maintenance
**Reinforcement Learning**
- Autonomous systems
- Game AI and simulations
- Trading algorithms
- Resource optimization
### Compliance & Standards
Our testing aligns with:
- **EU AI Act** (High-risk AI systems)
- **NIST AI Risk Management Framework**
- **ISO/IEC 42001** (AI Management System)
- **OWASP Machine Learning Security Top 10**
- Industry-specific regulations (FDA for medical AI, etc.)
### Deliverables
- Comprehensive ML security assessment report
- Adversarial robustness evaluation results
- Privacy and data leakage findings
- Bias and fairness analysis
- Proof-of-concept attacks (where applicable)
- Remediation recommendations and defenses
- Compliance gap analysis
- Ongoing monitoring recommendations
### Ideal For
- Organizations deploying AI in production
- ML platform and MLOps teams
- Fintech using ML for fraud/risk
- Healthcare AI and diagnostics
- Autonomous vehicle developers
- Content moderation and social media
- Government and defense AI systems
**Duration:** 6-12 weeks (depending on model complexity)
**Pricing:** Based on model type, access level, and scope
**Note:** Requires model access or API access for testing
