AI Red Team Audit
Advanced adversarial testing and security assessment for AI systems, machine learning models, and AI-powered applications. Our specialized red team identifies vulnerabilities, bias issues, and ensures your AI is secure and reliable.
What We Test
Comprehensive security assessment across all types of AI systems and applications
Large Language Models
ChatGPT-like models, custom LLMs, and conversational AI systems.
- • Prompt injection attacks
- • Jailbreaking attempts
- • Context manipulation
- • Output filtering bypass
Computer Vision Models
Image recognition, object detection, and visual AI systems.
- • Adversarial examples
- • Evasion attacks
- • Backdoor detection
- • Robustness testing
Recommendation Systems
Content recommendation, product suggestions, and personalization engines.
- • Bias amplification
- • Filter bubble creation
- • Manipulation attacks
- • Privacy leakage
Fraud Detection Systems
Financial fraud detection, anomaly detection, and security AI.
- • Adversarial evasion
- • False positive exploitation
- • Model inversion
- • Feature manipulation
Autonomous Systems
Self-driving cars, drones, robotics, and automated decision systems.
- • Sensor spoofing
- • Decision manipulation
- • Safety bypass
- • Edge case exploitation
Medical AI Systems
Diagnostic AI, drug discovery models, and healthcare decision support.
- • Diagnostic manipulation
- • Bias in treatment
- • Privacy violations
- • Safety critical failures
Red Team Attack Methodologies
Advanced techniques we use to test AI system security and robustness
Adversarial Attacks
Gradient-Based Attacks
FGSM, PGD, and C&W attacks to generate adversarial examples that fool models.
Black-Box Attacks
Query-based attacks when model internals are not accessible.
Transfer Attacks
Using surrogate models to generate attacks that transfer to target systems.
Physical Attacks
Real-world adversarial examples that work in physical environments.
Privacy & Data Attacks
Membership Inference
Determining if specific data was used in model training.
Model Inversion
Reconstructing training data from model outputs and parameters.
Model Extraction
Stealing model functionality through strategic queries.
Data Poisoning
Injecting malicious data to compromise model behavior.
Our AI Red Team Process
Systematic approach to identifying AI vulnerabilities and security weaknesses
Reconnaissance
Understanding the AI system architecture, data flow, and potential attack surfaces.
Threat Modeling
Identifying potential threats, attack vectors, and adversarial scenarios specific to your AI system.
Attack Execution
Implementing various attack techniques to test system robustness and security measures.
Impact Assessment
Evaluating the severity of vulnerabilities and providing actionable remediation strategies.
Ready for AI Security Testing
We're prepared to secure your AI systems and machine learning models. Be among the first to benefit from our specialized AI red team expertise.
Test Your AI Security Today
Protect your AI systems with comprehensive red team testing from security experts.
