What is AI red teaming?
AI red teaming is the practice of adversarially testing an AI system — a large language model (LLM), a chatbot, a retrieval-augmented (RAG) pipeline, or an autonomous agent — to find the ways it can be manipulated, misled, or abused before an attacker or a customer does. It goes beyond the model itself to the application built around it: the prompts, the tools the model can call, the data it can reach, and the guardrails meant to keep it in bounds.
Traditional penetration testing assumes deterministic software. AI systems are probabilistic and uniquely susceptible to attacks delivered in plain language. Rarefied pairs an attacker's mindset with an understanding of how these models actually fail, testing your AI features the way a motivated adversary would.
What we test
Every engagement is scoped to your system, but a typical AI red team covers:
- Prompt injection. Direct and indirect injection — including payloads hidden in the documents, web pages, and other content your model ingests — that hijack the model's instructions.
- Jailbreaks & guardrail bypass. Circumventing safety controls to elicit prohibited, harmful, or off-brand output.
- Sensitive data disclosure. Leakage of system prompts, secrets, other users' data, PII, and proprietary or training data.
- Excessive agency. For agentic systems, abuse of the tools, functions, and integrations the model can invoke — from unintended actions to full remote impact.
- RAG & data-store attacks. Poisoning, unauthorized retrieval, and access-control failures across the knowledge sources feeding the model.
- Insecure output handling. Downstream injection — XSS, SSRF, or code execution — when model output is trusted by the surrounding application.
- Denial of service & cost abuse. Resource- and token-exhaustion attacks that degrade availability or run up spend.
The application around the model
A secure model inside an insecure application is still a liability. We assess the full surface — authentication and authorization on AI endpoints, rate limiting, tenant isolation, logging and abuse monitoring, and the supply chain of models, plugins, and dependencies your system relies on.
Frameworks and standards
OWASP Top 10 for LLM Applications: The reference taxonomy for LLM and generative-AI risk.
MITRE ATLAS: Adversary tactics and techniques observed against AI systems.
NIST AI Risk Management Framework: Guidance for governing and managing AI risk.
The deliverable
You receive a report that documents every finding with a working proof of concept, the conditions needed to reproduce it, a business-impact rating, and concrete remediation — from prompt and guardrail hardening to architectural change. Where useful, we retest after fixes to confirm they hold.
Let Rarefied red team your AI before someone else does.