AI Red Teaming

What is AI Red Teaming?

AI red teaming is the practice of systematically probing artificial intelligence systems — including large language models, AI agents, and machine learning pipelines — through adversarial simulation to identify vulnerabilities, unsafe behaviors, and exploitable weaknesses before they are discovered by malicious actors. The term adapts the military and cybersecurity concept of red teaming to the unique threat model of AI systems, which can fail in ways that have no equivalent in traditional software: generating harmful content, leaking training data, following attacker instructions embedded in user input, or taking unauthorized actions through connected tools.

Description

AI red teaming encompasses multiple distinct evaluation types. Safety red teaming focuses on eliciting harmful, biased, or policy-violating outputs from a model — evaluating whether content filters and safety guardrails can be bypassed through adversarial prompting, roleplay framing, or multi-step jailbreaks. Security red teaming focuses on exploitability: assessing prompt injection vulnerabilities, model extraction risks, data leakage from training sets, and authentication weaknesses in AI deployment infrastructure. Agentic red teaming addresses agentic AI systems specifically, evaluating how an autonomous agent responds to adversarial inputs across its full action space — not just its language outputs. Microsoft, Anthropic, OpenAI, and Google all conduct internal AI red teaming on their models before release, but enterprise deployments of these models face organization-specific risks that require their own red team exercises. MCP security assessments are increasingly a component of AI red teaming as agentic tool integrations expand the exploitable attack surface.

Usage and Examples

An enterprise AI red team engagement might include: crafting adversarial prompts that cause a customer-facing chatbot to reveal its system prompt or confidential pricing data; testing whether an AI coding assistant can be manipulated via malicious code comments to recommend insecure patterns; embedding hidden instructions in documents submitted to an AI document review system; and evaluating whether an AI agent can be induced to take unauthorized actions against connected systems. Findings from AI red team exercises directly inform security controls: which inputs require stricter validation, which tool permissions should be reduced, where human review checkpoints should be inserted, and which AI use cases carry unacceptable risk for the organization's threat model. Evolve Security's guide to testing for prompt injection provides hands-on methodology for one of the most critical AI red teaming scenarios.

How Does This Relate to Penetration Testing?

AI red teaming is the adversarial testing component of AI Penetration Testing engagements. While AI pen testing covers the full security assessment lifecycle — scoping, testing, documentation, and remediation guidance — the red team component specifically involves creative, multi-vector adversarial simulation against AI systems in conditions that approximate real attacker behavior. This includes chained attacks that combine AI vulnerabilities with traditional application and network weaknesses, testing AI systems as both targets and potential weapons within a broader attack scenario. As organizations deploy AI into production at scale, AI red teaming is becoming as foundational to a mature security program as traditional network penetration testing. Evolve Security offers structured AI Penetration Testing engagements that include adversarial AI red teaming, helping organizations understand how their AI systems behave under attack before adversaries find out first.