Topic starter
25/10/2026 11:03 am
TL;DR: Prompt injection, sensitive information disclosure, excessive agency, and weak tool controls remain the main LLM red teaming risks according to Protecto. AI assurance has to be rerun whenever models, data sources, or permissions change.
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: 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.
Q: How should security teams test enterprise LLMs for prompt injection risk?
A: Test the model inside the real application path, not in isolation.
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.
Glossary terms: Prompt Injection (Agentic), LLM Red Teaming, Excessive agency, Retrieval-augmented Generation
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This topic was modified 7 days ago by NHI Mgmt Group
27/10/2026 7:16 pm
LLM red teaming is becoming a governance discipline, not just a testing technique. The core issue is that language models fail in ways that ordinary application assurance does not model, especially when prompts, retrieval layers, and tool permissions intersect. That creates a governance gap between what the AI is allowed to do and what the surrounding controls assume it will do. Practitioners need testing that measures behaviour under adversarial input, not just software correctness.
A few things that frame the scale:
- 70% of organisations grant AI systems more access than they would give a human employee performing the exact same job, according to the 2026 Infrastructure Identity Survey.
- A May 2025 Gartner poll of 147 CIOs and IT leaders found that 24% had already deployed AI agents, 50% were experimenting and 17% planned to deploy by the end of 2026.
A question worth separating out:
Q: What should security teams do when AI systems change after launch?
A: They should rerun red-team exercises whenever models, retrieval sources, connectors, or agent permissions change. AI assurance degrades quickly when the workflow evolves, so monitoring and adversarial testing must be continuous rather than tied to a single release.
👉 Read our full editorial: LLM red teaming is now essential for prompt and data risk
This post was modified 7 days ago by NHI Mgmt Group