AI Red Teaming & AI Audit:
LLM Security Assessment

AI red teaming is a structured security assessment that simulates real-world attacks against AI systems, including large language models (LLMs), machine learning pipelines, and AI-powered applications. Unlike traditional penetration testing, AI red teaming specifically targets prompt injection vulnerabilities, model poisoning risks, data exfiltration through AI outputs, and adversarial manipulation of AI decision-making. According to the OWASP Top 10 for LLM Applications (2025), prompt injection is the number one critical vulnerability found in production AI systems.

Traditional penetration testing misses AI-specific attack vectors. The McKinsey Lilli breach in February 2026 demonstrated this clearly: an AI agent achieved full read-write access to 46.5 million chat messages and 728,000 files in just 2 hours through SQL injection in unauthenticated AI API endpoints — vulnerabilities that McKinsey's own internal scanners failed to detect. AI systems introduce unique risks including prompt injection, model poisoning, training data extraction, and system prompt manipulation that require specialised testing methodologies.

The EU AI Act requires adversarial testing (red teaming) for high-risk AI systems, with full compliance mandatory by August 2, 2026. High-risk classifications cover AI used in critical infrastructure, employment, credit decisions, education, and law enforcement. Non-compliance penalties reach up to €15 million or 3% of global annual turnover for high-risk obligations (up to €35 million or 7% for prohibited AI practices). Our AI red teaming assessments map directly to EU AI Act Article 9 risk management requirements and provide documentation suitable for regulatory review.

Our methodology aligns with three leading AI security frameworks: the OWASP Top 10 for LLM Applications (2025 edition), the NIST AI Risk Management Framework (AI RMF), and ENISA guidelines for securing AI systems. We also incorporate MITRE ATLAS (Adversarial Threat Landscape for AI Systems) tactics and techniques for comprehensive threat modelling.

Yes. AI-powered development tools have become a significant enterprise attack surface. In February 2026, Check Point Research disclosed CVE-2025-59536 (CVSS 8.7) in Claude Code, enabling remote code execution via malicious project configurations. A single compromised developer account can inject malicious AI configurations that affect entire development teams — a supply chain attack vector. We test all AI-integrated development workflows as part of our assessment.

A focused AI security configuration review typically takes 2-4 weeks depending on the number of AI systems and complexity of your infrastructure. Comprehensive AI red teaming engagements that include adversarial prompt testing, model poisoning assessment, and full attack simulation take 4-8 weeks. We provide immediate escalation of critical findings, with the full report and remediation roadmap delivered within one week of testing completion.

We work with transparent fixed prices based on the defined scope — number of AI systems, complexity of integrations, and depth of testing required. No hidden costs. You receive a detailed quote during scoping before any work begins. Contact us for a non-binding assessment of your AI infrastructure.