Expose weaknesses in model behavior, data pipelines, and AI-drive workflows before attackers do. Halborn's AI Red Teaming tests the full stack - models, inputs, integrations, and people – to strengthen trust and reduce real-world risk.
// Specialized AI offense to harden AI defense.
Teams combine offensive security, ML engineering, and prompt-engineering knowledge to assess attacks unique to AI systems
Testing covers model behavior, data inputs, pipelines, API integrations, and human-in-the-loop risks — not just the model weights
Scoped, authorized adversarial tests that prioritize safety, data integrity, and operational continuity
Evaluate risks from prompt injections, model poisoning, data poisoning, malicious LLMs, and AI-driven social engineering
Identify behavioral failure modes, insecure integrations, and exploitable data flows affecting both model outputs and downstream systems
Deliver prioritized, developer-friendly fixes: prompt hardening, input sanitization, monitoring rules, and governance changes
Demonstrate due diligence to stakeholders by validating AI controls, logging, incident playbooks, and governance around model use