AI Red Teaming
AI red teaming is adversarial testing of AI-enabled systems to identify how models, prompts, agents, tools, retrieved content, and connected workflows can be manipulated, misused, or pushed outside intended boundaries.
For production AI products, red teaming is not only about jailbreak prompts. It examines what the AI system can access, decide, trigger, retrieve, expose, or delegate through the product around it.
That system may include an LLM, a RAG pipeline, tool calls, MCP servers, approval flows, customer data, internal APIs, support workflows, or agent actions. The test is useful when those parts behave differently under adversarial input than they do in happy-path demos.
A good AI red team exercise gives engineering and product teams evidence: which behaviors were tested, what failed, what stayed inside the intended boundary, and which controls need to change before the feature is trusted in production.
What AI red teaming tests
AI red teaming maps the ways instructions, data, tools, and workflow permissions interact. The useful question is not just whether a model can be tricked, but what the product lets that tricked model do.
These categories help teams scope realistic tests without turning the work into a payload library.
Prompt and policy bypass
Testing whether user input can override intended instructions, policies, or role boundaries.
Indirect prompt injection
Testing whether documents, tickets, web pages, emails, or retrieved content can steer model behavior.
RAG manipulation
Testing whether poisoned or misleading retrieved content changes answers, citations, or downstream decisions.
Tool and agent misuse
Testing whether the AI can call tools, APIs, or agent actions outside the user's intent or permission.
MCP and tool boundaries
Testing whether MCP servers and tools expose more files, APIs, resources, or actions than the feature needs.
Approval and workflow bypass
Testing whether approval steps, confirmations, and human review can be skipped through multi-step agent paths.
How AI red teaming fits product security
AI red teaming works best when it is scoped around product behavior. The team defines what the AI feature is allowed to do, then tests whether those boundaries hold under adversarial prompts, content, tool responses, and workflows.
Map the AI system boundary
List models, prompts, RAG sources, tools, MCP servers, APIs, approvals, and data paths that shape behavior.
Define intended behavior
Clarify what the feature should answer, access, refuse, ask approval for, or never do.
Run controlled adversarial scenarios
Exercise direct input, indirect content, retrieval, tool calls, and agent workflows with safe, bounded tests.
Report evidence and remediation paths
Document impact, reproduction context, affected boundaries, and fixes that engineering teams can verify.
What stays useful
Public Appsecco AI/MCP security resources
A public checklist for reviewing MCP server security, tool safety, auth boundaries, and data exposure paths.
A testing client and proxy for exercising MCP servers during security reviews.
Intentionally vulnerable MCP servers for learning attack paths and validating defensive controls.
Related AI security terms
Risks and controls for model behavior, prompts, retrieval, tools, and connected systems.
How malicious instructions can enter prompts through users or untrusted content.
Security controls for agents that use tools, memory, approvals, and workflow access.
Security considerations for MCP servers, clients, tools, transports, and authorization boundaries.
Explore AI security testing
Related AI security services and resources
Move from AI security concepts into testing scope, agent risks, prompt injection, MCP exposure, and practical assessment paths.
AI & MCP Security Testing
Product security testing for AI apps, agent workflows, MCP tools, prompts, and connected data sources.
LLM Integration Security Testing
Security testing for LLM features, RAG workflows, prompt handling, tool calls, and connected data exposure.
AI Agent Security Testing
Assessment of agent workflows, tool permissions, approval boundaries, memory handling, and autonomous actions.
MCP Server Security Testing
Scoped testing for transport security, tool safety, prompt injection, OAuth hygiene, and access boundaries.
AI Red Teaming for LLM Applications
How to scope adversarial testing for LLM apps, RAG, agents, tools, MCP, and workflow actions.
AI Red Teaming vs AI Security Testing
How adversarial AI behavior testing fits with broader product and system security testing.
LLM Security
Risks and controls for LLM applications, RAG systems, embeddings, and model-connected workflows.
Prompt Injection
How malicious instructions enter prompts through users, documents, retrieved content, and tool output.
AI Agent Security
Security controls for agents that use tools, memory, approvals, and connected workflows.
Safe next step
Talk through your AI red teaming scope.
No commitment required.
Share the AI feature, tools, data paths, and workflow boundaries you care about. We will help frame what should be tested and where AI red teaming fits.
Start a conversationor Open the MCP checklist first