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06/09/2026 5:18 pm
TL;DR: AI red teaming is the process of testing AI systems with adversarial inputs to expose prompt injection, data leakage, unsafe actions, and tool misuse before attackers do, according to LEVO. As autonomous AI scales faster than governance, the real control problem is not model quality alone but whether delegated systems can be broken under realistic pressure.
NHIMG editorial — based on content published by LEVO: LLMjacking: How Attackers Hijack AI Using Compromised NHIs
By the numbers:
- 88% of organisations now use AI in at least one business function, up from 78% a year earlier.
- 62% are already experimenting with autonomous AI agents, and nearly a quarter have begun scaling agentic systems in production.
- AI incidents are rising sharply, increasing by roughly 50% in just 6 months according to OECD tracking.
Questions worth separating out
Q: What breaks when AI agents are not red teamed before deployment?
A: Without adversarial testing, teams discover prompt injection, tool misuse, and data leakage only after the system is already trusted with real access.
Q: Why do delegated credentials increase risk when AI agents and users are not clearly separated?
A: Delegated credentials can blur the boundary between user intent and agent autonomy.
Q: How do security teams know if AI red teaming is working?
A: AI red teaming is working when testing finds real prompt injection paths, over-scoped integrations, and policy gaps before attackers do, and when fixes are re-tested successfully after model or workflow changes.
Practitioner guidance
- Define red team scope around delegated AI privileges Map every tool, API, connector, and data source an AI system can reach, then test whether malicious prompts can drive those privileges beyond intended use.
- Test for indirect prompt injection in real workflows Use emails, documents, retrieved content, and chat messages as attack carriers, then verify whether the agent follows hidden instructions embedded in ordinary business content.
- Treat AI outputs and tool calls as audit evidence Log prompts, retrieved context, model responses, tool actions, and approval events so red team findings can be reproduced and investigated.
What's in the full article
LEVO's full article covers the operational detail this post intentionally leaves for the source:
- Detailed breakdown of red teaming methods for prompt injection, jailbreaks, and indirect instruction attacks
- Examples of multi-step testing against tool-using agents, including data leakage and unsafe action scenarios
- Operational challenges in continuous testing for non-deterministic and multi-agent workflows
- LEVO's view of how monitoring and runtime enforcement complement red teaming across the AI lifecycle
👉 Read LEVO's analysis of AI red teaming for autonomous AI security →
AI red teaming and agentic systems: are your controls keeping up?
Explore further
06/09/2026 5:19 pm
AI red teaming is now an identity control problem as much as an AI assurance problem. When an agent can read data, call tools, or trigger workflows, it functions as a delegated non-human identity with runtime authority. That changes the governance question from model quality to privilege containment, approval boundaries, and post-issuance monitoring. The practical conclusion is that AI red team results should inform IAM and PAM decisions, not sit only in an AI risk register.
A question worth separating out:
Q: How should security teams govern AI agents that can access enterprise systems?
A: Security teams should govern AI agents as non-human identities with explicit ownership, scoped privileges, and continuous monitoring. The control set should include inventory, task-bound credentials, audit trails, and revocation paths. If an agent can call tools or touch production systems, it belongs in the same governance model as service accounts and other machine identities.
👉 Read our full editorial: AI red teaming is becoming the control plane for agentic AI security
