Attack your AI before attackers do
Structured adversarial testing of LLM applications, AI agents, and machine learning systems — uncovering vulnerabilities that automated scanners and conventional pen tests miss entirely.
What is AI Red Teaming — Adversarial Testing for AI Systems?
AI red teaming is structured adversarial testing of artificial intelligence systems to identify security vulnerabilities, safety failures, and exploitable behaviours. Unlike traditional penetration testing, AI red teaming focuses on LLM-specific attack techniques: prompt injection, jailbreaks, model extraction, training data extraction, agent hijacking, and adversarial input attacks against ML models.
Why AI systems fail differently than traditional software
- LLMs exhibit emergent failure modes — new attack techniques appear continuously and cannot be fully anticipated at build time
- Traditional penetration testing methodologies do not cover LLM-specific risks: jailbreaks, prompt injection, model extraction, or data poisoning
- AI safety evaluations (internal testing) and AI security red teaming are different — safety testing finds harmful outputs, security red teaming finds exploitable vulnerabilities
- AI systems can behave differently under adversarial pressure than under normal use — creating vulnerabilities invisible in standard QA testing
A four-step operational model
Scope & Threat Modelling
Define the AI attack surface, identify adversary personas (external attackers, malicious insiders, competitor actors), and map out the threat scenarios most relevant to your deployment.
- AI attack surface mapping
- Adversary persona definition
- Priority threat scenario selection
Automated Adversarial Testing
Run systematic adversarial test suites covering the OWASP LLM Top 10, known jailbreak techniques, and injection attack patterns against all in-scope AI endpoints.
- OWASP LLM Top 10 testing
- Jailbreak resistance evaluation
- 100+ prompt injection scenarios
Manual Expert Testing
Human red teamers attempt novel attack chains, multi-turn manipulation, indirect injection via realistic data sources, and agent hijacking scenarios.
- Novel attack chain development
- Multi-turn adversarial conversation
- Agent tool chain exploitation
Finding Synthesis
Risk-ranked findings with exploitability evidence, business impact assessment, and prioritised remediation roadmap.
- Exploitability-ranked report
- Business impact mapping
- Remediation priority roadmap
Outcomes for security teams
AI systems fail in ways developers cannot anticipate
Emergent LLM behaviours under adversarial pressure are not discoverable through code review or standard QA — red teaming is the only reliable method.
Regulations increasingly require adversarial AI evaluation
NIST AI RMF, EU AI Act, and emerging sector guidance explicitly recommend adversarial testing as part of responsible AI deployment.
The cost of a jailbroken production AI is high
A successfully exploited LLM in production can damage brand reputation, expose sensitive data, and trigger regulatory scrutiny simultaneously.
Direct answers
What is AI red teaming?+
AI red teaming is structured adversarial testing of AI systems — using attacker-perspective techniques to find vulnerabilities, safety failures, and exploitable behaviours in LLMs and AI applications.
How is AI red teaming different from AI safety testing?+
AI safety testing evaluates harmful, biased, or policy-violating outputs. AI security red teaming evaluates exploitable vulnerabilities — prompt injection, data exfiltration, model extraction, and agent compromise — from an attacker perspective.
How is AI red teaming different from traditional penetration testing?+
Traditional pen testing focuses on CVEs, misconfiguration, and access control vulnerabilities in web and network infrastructure. AI red teaming specifically targets LLM-layer risks that require different techniques and specialised expertise.
Does Threatstealth offer automated AI red teaming?+
Yes — automated adversarial test suites run continuous regression testing against your AI endpoints. Manual expert red teaming is available for deeper, novel attack chain development.