Tech Stack-Specific Challenges

Your Technology, Your Challenges

We create challenges based on your actual infrastructure:

• AWS/Azure/GCP cloud environments
• Kubernetes & Docker containers
• Your specific frameworks (Django, React, .NET, etc.)
• Databases you use (PostgreSQL, MongoDB, MySQL)
• CI/CD pipeline vulnerabilities
• Microservices architecture flaws
• API security specific to your design

Industry-Specific Scenarios

Realistic Attack Simulations

Challenges that mirror real threats in your sector:

• Healthcare: HIPAA compliance, patient data protection
• Finance: PCI-DSS, payment system security
• Retail: E-commerce vulnerabilities, POS attacks
• Manufacturing: ICS/SCADA security, IoT
• Government: Classified data handling
• SaaS: Multi-tenant security, API abuse

Vulnerability Research

Latest Attack Techniques

Stay ahead of emerging threats:

• Recent CVE exploitations (0-days)
• Advanced persistence techniques
• Supply chain attack simulations
• Modern ransomware tactics
• Social engineering campaigns
• Insider threat scenarios
• Zero-trust architecture testing

Large Language Model (LLM) Security

Test your team's ability to secure AI chatbots and assistants

• Prompt Injection: Direct and indirect prompt attacks
• Jailbreaking: Bypassing content filters and guardrails
• Data Extraction: Recovering training data and PII
• Context Manipulation: Exploiting RAG systems
• Agent Exploitation: Attacking AI agent workflows
• Plugin Vulnerabilities: Tool-use security issues

Machine Learning Model Security

Evaluate ML model robustness and defense capabilities

• Adversarial Examples: Crafting inputs to fool models
• Model Extraction: Stealing model architecture/weights
• Backdoor Detection: Finding poisoned models
• Membership Inference: Identifying training data
• Model Inversion: Reconstructing sensitive training data
• Fairness Attacks: Exploiting bias vulnerabilities

AI Pipeline & Infrastructure

Secure the entire ML lifecycle

• Training Pipeline: Data poisoning, supply chain attacks
• Model Registry: Access control, version tampering
• Deployment Security: Container escape, API abuse
• MLOps Security: CI/CD pipeline vulnerabilities
• Feature Store: Data leakage, access violations
• Monitoring Evasion: Bypassing drift detection

Benchmarking & Metrics

Quantifiable security assessment results

• Red Team Scores: Offensive capability ratings
• Blue Team Scores: Detection and response metrics
• MITRE ATLAS Mapping: Adversarial ML threat coverage
• Time-to-Exploit: Speed of vulnerability discovery
• Defense Effectiveness: Mitigation success rates
• Comparison Reports: Industry benchmark comparisons

Prompt Injection Expert

Difficulty: Medium

Bypass an LLM-powered customer service chatbot to access admin functions and extract customer PII from the knowledge base.

Tests: Indirect prompt injection, RAG exploitation

Model Extraction

Difficulty: Hard

Given only API access to a proprietary ML model, extract enough information to clone it with >90% accuracy.

Tests: Black-box model stealing, query optimization

Adversarial Defense

Difficulty: Expert

A facial recognition system is deployed. Craft adversarial patches that allow unauthorized access while appearing normal to humans.

Tests: Adversarial ML, physical-world attacks