AI Red Teaming
Adversarial Simulation Against AI Systems
Full-scope adversarial simulation targeting AI-powered applications, autonomous agents, and LLM pipelines.
What is AI Red Teaming?
AI Red Teaming goes beyond vulnerability scanning of AI systems — it simulates how a determined adversary would attempt to manipulate, deceive, or abuse your AI-powered products and infrastructure to achieve real-world objectives. Where AI Security testing identifies vulnerabilities, AI Red Teaming proves what those vulnerabilities enable in practice.
Our operators conduct multi-session, goal-directed attacks against your AI applications: attempting to exfiltrate system prompts, hijack AI agent actions, poison RAG pipeline responses, extract training data, or cause the system to take actions it was explicitly designed to prevent. We document not just what we found, but what we achieved — the adversarial outcomes your current controls failed to prevent.
This service is particularly critical for organisations using AI agents with real-world tool access — where a successful attack can result in data exfiltration, unauthorised transactions, or actions taken in the real world on behalf of a manipulated AI system.
Why it matters
- AI agents with tool access (email, databases, APIs, code execution) can be hijacked to perform real-world damage through crafted inputs alone
- Multi-turn manipulation attacks require no technical exploit — they use natural language across multiple conversation turns to erode guardrails incrementally
- RAG pipeline poisoning allows attackers to persistently influence LLM behaviour by injecting content into your knowledge base
- Indirect prompt injection — via documents, emails, or web pages the AI reads — enables attacks that don't require any direct access to the AI application
- Safety guardrails and system prompt instructions are not security controls — they are defeated routinely by operators with basic red team training
Our methodology
1. Objective Setting & System Profiling
We work with you to define adversarial objectives — what a real attacker would want to achieve with your AI system. We then profile the system's capabilities, tool integrations, and trust boundaries to plan realistic attack scenarios.
2. Direct Attack Campaigns
Systematic multi-turn campaigns against your LLM application: identity manipulation, role-play exploitation, instruction override, false context injection, and safety boundary probing across all user-accessible interfaces.
3. Indirect & Supply Chain Attacks
Testing indirect injection vectors: documents fed to the AI, web content retrieved via browsing tools, external API responses processed by the model, and email content in AI-assisted workflows — anywhere untrusted content reaches the LLM context window.
4. Agentic Abuse & Goal Hijacking
For AI agents, we attempt goal hijacking (redirecting agent actions to attacker objectives), tool abuse (exploiting agent tool access beyond intended scope), and chain-of-thought manipulation to cause the agent to reason its way into harmful actions.
Frequently asked questions
How is AI Red Teaming different from AI Security testing?
AI Security testing systematically checks for known vulnerability classes (OWASP LLM Top 10). AI Red Teaming is goal-directed — our operators try to achieve specific adversarial outcomes using any technique available, including novel approaches not in any standard checklist. Red teaming reveals what's actually achievable, not just what's theoretically vulnerable.
Do you follow any recognised framework for AI Red Teaming?
Yes. We follow MITRE ATLAS (Adversarial Threat Landscape for AI Systems), NIST AI RMF guidance on adversarial ML, and the emerging practices from Microsoft's AI Red Team. We also draw on our own research into novel LLM attack techniques.
Our AI system has extensive guardrails — is red teaming still worthwhile?
Especially so. Guardrails are exactly what red teaming stress-tests. In our experience, the majority of guardrail implementations can be bypassed with moderate effort. Confirming their effectiveness — or finding their limits — requires adversarial operators, not conformance testing.
How long does an AI Red Team engagement take?
Typically 5–10 business days depending on system complexity. Agentic systems with multiple tool integrations take longer to test thoroughly. We agree objectives and success criteria before the engagement begins.
Can you red team AI systems still in development?
Yes, and this is often the most cost-effective point to engage. Findings from pre-release red teaming can be addressed in architecture and prompt design — much cheaper than remediating a live system post-launch.
Deliverables
Red Team Campaign Report
Full narrative of attack campaigns, objectives pursued, and outcomes achieved
Attack Playbook
Documented attack techniques and payloads that succeeded against your system
Guardrail Bypass Evidence
Transcript and video captures of successful safety boundary violations
Agentic Risk Assessment
Analysis of real-world harm potential from tool-equipped agent manipulation
Control Recommendations
Architectural and prompt-level controls to mitigate identified attack paths
Industries served
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