Generative AI Red Team - Insight
Prompt, Tool, and Privilege Chains in AI Agents
Do not evaluate an AI agent only by its responses. Test prompt, RAG, tool, MCP, data, and privilege flows end to end to verify misuse paths.
The security of an AI agent cannot be measured only by the accuracy of its responses. The content it can read, tools it can invoke, data it can access, and actions it can perform all belong to the same runtime flow. Security testing should cover that entire chain.
The wrong framing
Limiting evaluation to hallucinations and response filtering leaves risks from connected systems unseen. When an email, web page, RAG record, or tool description carries a hidden instruction, the agent can move beyond the user’s intent.
The right framing
In indirect prompt injection, the attacker does not place the instruction directly in the chat box; it is embedded in content the model will read later. The OWASP LLM Top 10 highlights this attack class. Testing should trace how untrusted content influences system instructions, data access, and tool use.
Risk increases when private-data access, untrusted content, and the ability to perform external actions meet in one agent session. The architectural objective is to separate these conditions and constrain high-impact actions through explicit approval, least privilege, and detailed logging.
In the field: cases
The EchoLeak case demonstrated a chain in which Microsoft 365 Copilot processed an indirect instruction from an email and enabled data exfiltration. Similar research affecting GitHub Copilot Chat and Slack AI showed the same pattern across code repositories and collaboration tools.
These incidents did not depend on bypassing a single defense. Content processing, link rendering, allowed services, and external communication were chained together. The combined behavior of controls determined the outcome.
The limit of mitigation
Strengthening the system prompt, adding classifiers, or using blocklists can make an attack harder. Architectural risk remains when an agent has unnecessarily broad data and action permissions. Controls must operate together at the session, tool, and data-source levels.
Delivery and verification
The Generative AI Red Team tests prompt, RAG, tool, MCP, and privilege flows within authorized boundaries. A verified misuse path is reported with a safe proof of concept, affected data, and business impact; chain-breaking controls are prioritized for closure and retested.
The right starting point
Begin by inventorying the agent workflow, tool catalog, permission matrix, and data sources. AI Model Supply-Chain Assurance fits model provenance and deployment pipelines, while the Generative AI Red Team fits the runtime behavior of a client-owned agent.
Concepts and abbreviations in this article
An attack in which instructions are embedded in a document, email, web page, or tool description that a model later reads.
The combination of private-data access, untrusted content, and the ability to perform external actions within one agent session.
An open protocol that connects AI applications to external tools and data sources through a standard interface.