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01/08/2026 10:44 pm
TL;DR: Gen AI red teaming simulates prompt injection, jailbreaks, data leakage, poisoning, and excessive agency across LLMs and agentic systems to expose behavioural failure modes before attackers do, according to Akto. The core issue is that AI security now depends on testing dynamic model and tool behaviour, not just static code or infrastructure.
NHIMG editorial — based on content published by Akto: Gen AI Red Teaming: A Complete Guide to Testing and Securing AI Systems
Questions worth separating out
Q: How should security teams test generative AI systems for real-world abuse?
A: They should test the full execution path, not just the model prompt.
Q: Why do AI agents complicate privilege management?
A: AI agents complicate privilege management because they can execute actions autonomously, chain tools, and consume access without the normal human pauses that create review opportunities.
Q: What do organisations get wrong about prompt injection?
A: They often treat it as a purely content-filtering problem.
Practitioner guidance
- Define test scope around the full AI execution chain Include prompts, retrieval layers, connected tools, agent handoffs, and downstream systems that consume model output.
- Map AI test cases to named risk categories Use OWASP Agentic AI Top 10 and related frameworks to classify findings such as prompt injection, system prompt leakage, excessive agency, and unbounded consumption.
- Review delegated permissions and secrets exposure Inventory the tokens, API keys, and service accounts an AI system can reach, then reduce them to the smallest viable scope.
What's in the full article
Akto's full blog covers the operational detail this post intentionally leaves for the source:
- Hands-on attack simulation patterns across prompts, RAG pipelines, and agent workflows for teams building a red team programme.
- A step-by-step process for scoping, executing, and iterating Gen AI red team exercises in production-like environments.
- The article's OWASP LLM Top 10 mapping table, which is useful for turning findings into remediation work items.
- Examples of mitigation patterns for prompt hardening, output controls, and testing cadence.
👉 Read Akto's guide to Gen AI red teaming and OWASP LLM risks →
Gen AI red teaming and OWASP LLM risks: are your controls ready?
Explore further
01/08/2026 10:53 pm
Gen AI red teaming is becoming a control function, not a specialist exercise. The article shows why AI systems can no longer be treated as static software assets. Once prompts, retrieval, and tool calls become part of the execution chain, security testing has to move closer to continuous control validation. That aligns with NIST AI Risk Management Framework thinking and with OWASP Agentic AI Top 10 coverage. Practitioners should read this as a signal that AI red teaming belongs in governance, not only in lab testing.
A question worth separating out:
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. The strongest signal is repeatable reduction in exposed authority, not a lower number of red-team findings on its own.
👉 Read our full editorial: Gen AI red teaming is becoming essential for AI governance
