AI Red Teaming

AI Red Teaming in Practice

Adversarial testing of AI systems from an attacker’s perspective. AI red teaming goes beyond traditional penetration testing — it combines technical exploitation of model vulnerabilities with creative adversarial techniques to test the boundaries of AI system safety, alignment, and security controls. Simulates real-world attacks to find weaknesses before adversaries do.

What We Test

Jailbreak and Guardrail Bypass

• Systematic testing of content filters, safety alignment, and usage policies
• Known and novel bypass techniques

System Prompt Extraction

• Techniques to recover system prompts, hidden instructions, and confidential configuration from deployed models

Multi-Turn Manipulation

• Complex attack chains that build context over multiple interactions
• Gradually shifting model behavior outside intended boundaries

Cross-Context Attacks

• Exploiting shared infrastructure, cached contexts, or session bleed
• Accessing other users’ data or influencing other sessions

Capability Elicitation

• Testing whether AI systems can be coerced into demonstrating capabilities intended to be restricted

Business Logic Abuse

• Manipulating AI-driven pricing and recommendation systems
• Content moderation and classification model abuse
• Fraud detection and risk scoring system manipulation
• Any AI-powered business process that makes consequential decisions

Deliverables

• AI red team engagement report with attack narratives and reproduction steps
• Guardrail and safety control effectiveness assessment
• Prioritized recommendations for improving model safety and security posture