LLM red teaming: what practitioners need to test first

TL;DR: LLMs now power workflows and AI agents, but prompt injection, sensitive information disclosure, excessive agency, and weak tool controls create risks that traditional application security often misses, according to Protecto. Continuous red teaming is becoming the practical way to expose model behaviour, data leakage, and unsafe actions before attackers do.

Editorial analysis by NHI Mgmt Group, based on content published by Protecto: “LLM Red Teaming Checklist for Prompt Injection, PII Leakage, and Tool Abuse”.

By the numbers:

• 78% of organisations reported using AI in at least one business function in 2024, rising to 88% in 2025, according to McKinsey’s State of AI research cited by Protecto.
• LLM red teaming should account for the fact that 88% of organisations reported using AI in at least one business function in 2025, according to McKinsey’s State of AI research cited by Protecto.

Key questions

Q: How should security teams test enterprise LLMs for prompt injection risk?

A: Test the model inside the real application path, not in isolation.

Q: Why do LLM applications create new data leakage risks for identity teams?

A: LLM applications can expose sensitive data when users paste secrets, when agents retrieve privileged context, or when responses echo internal material back to users.

Q: What breaks when AI agents have broader access than their tasks require?

A: Over-privileged agents break segregation of duties, weaken auditability, and expand blast radius across transactions, data lookups, and workflow triggers.

Practitioner guidance

• Test prompt injection across every input path Include direct prompts, uploaded documents, emails, web content, and retrieved knowledge so you can see where the model follows attacker-supplied instructions instead of policy.
• Map sensitive data flow through the full AI lifecycle Track where PII, financial data, customer records, and internal documents enter, persist, and reappear in prompts, logs, embeddings, and responses.
• Constrain tool access to the minimum task scope Limit which APIs, databases, and enterprise applications an agent can reach, and verify that sensitive actions still require explicit authorisation.

Bottom line: LLM red teaming matters because language-model risk often appears in instruction handling, retrieval, and tool use rather than in traditional code flaws.

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