Gen AI red teaming is becoming essential for AI governance

At a glance

What this is: This is a guide to Gen AI red teaming, with a focus on how adversarial testing reveals prompt injection, jailbreak, leakage, poisoning, and agentic misuse in AI systems.

Why it matters: It matters because practitioners now need governance that covers AI models, tool use, and delegated actions, especially where LLMs can touch sensitive data or make downstream decisions.

👉 Read Akto's guide to Gen AI red teaming and OWASP LLM risks

Context

Gen AI red teaming is the practice of probing AI systems with adversarial prompts, data, and workflow abuse to find failure modes before real attackers do. The key governance problem is that LLMs and agentic systems behave probabilistically, so conventional application testing misses the way harmful output, tool misuse, and data leakage emerge across multiple interactions. For identity teams, that becomes an access-control question as soon as models can act on behalf of users, service accounts, or other non-human identities.

The article sits in the wider shift from model-only security to full AI system governance, where prompts, retrieval pipelines, plugins, and execution privileges all form part of the attack surface. That is why OWASP Agentic AI Top 10 and NIST AI Risk Management Framework guidance matter here, alongside identity controls for secrets, API access, and delegated permissions.

Key questions

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. That means probing retrieval, tool use, output handling, and any delegated actions the system can perform. The goal is to reproduce how an attacker would move from manipulation to impact, then capture whether the system leaks data, ignores guardrails, or performs unauthorised actions.

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. That makes privilege decisions faster, less visible, and harder to reverse. Security teams need policy, logging, and revocation designed for machine speed, not just human approvals.

Q: What do organisations get wrong about prompt injection?

A: They often treat it as a purely content-filtering problem. In practice, prompt injection is an instruction-trust problem that becomes serious when the chatbot can act on behalf of the organisation. The fix is not just blocking bad text. It is constraining what the model can access, what it can call, and what it can change.

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.

Technical breakdown

How gen AI red teaming maps to real attack paths

Gen AI red teaming is not a single test. It is a set of adversarial exercises that tries to reproduce how an attacker would steer a model, a retrieval layer, or an agentic workflow into unsafe behaviour. The important distinction is that the target is not just the model output. It is the whole execution chain, including prompts, memory, connectors, tools, and downstream systems that consume the response. That is why red team coverage has to include direct and indirect prompt injection, system prompt extraction, unsafe output handling, and tool abuse. In agentic environments, the same weakness can create very different outcomes depending on whether the model can merely reply or can also call APIs and change state. Practical implication: test end-to-end workflows, not just standalone model prompts.

Practical implication: test end-to-end workflows, not just standalone model prompts.

Why agentic AI changes the risk model for access and privilege

Agentic systems change the risk profile because they combine language understanding with permissioned action. Once an AI system can call tools, access APIs, or move through multi-agent pipelines, an attacker does not need perfect control. A single successful exploit can be enough to trigger real-world actions at machine speed. That creates an identity problem as much as an AI problem, because every tool, token, and delegated permission becomes part of the trust boundary. In practice, this means least privilege, scoped tokens, and explicit approval logic matter far more than prompt filtering alone. It also means the security team has to understand which identities, secrets, and service accounts the agent can reach. Practical implication: treat agent permissions as privileged access, not as a harmless implementation detail.

Practical implication: treat agent permissions as privileged access, not as a harmless implementation detail.

OWASP LLM Top 10 and AI security testing coverage

The article’s mapping to OWASP LLM Top 10 reflects the current reality of AI testing. Red teams now need to probe for prompt injection, sensitive information disclosure, model and data poisoning, misinformation, improper output handling, embedding weaknesses, system prompt leakage, supply chain risk, unbounded consumption, and excessive agency. That list is useful because it turns vague AI risk into specific testable failure modes. It also shows why security testing has to include regression coverage and continuous retesting, since new prompts, models, and connectors can reintroduce old failures. The most important practical point is that AI security testing should produce evidence that can be translated into governance decisions, not just a list of interesting edge cases. Practical implication: align red team findings to a recognised risk taxonomy so remediation is trackable.

Practical implication: align red team findings to a recognised risk taxonomy so remediation is trackable.

Threat narrative

Attacker objective: The attacker wants to turn a language model or agentic workflow into a controllable execution path for data theft, policy bypass, or unauthorised system action.

1. Entry begins with adversarial prompts, poisoned context, or malicious files designed to influence how the model interprets instructions.
2. Escalation occurs when the model crosses from text generation into tool calls, retrieval access, or multi-agent coordination with delegated permissions.
3. Impact follows when the system leaks data, performs unauthorised actions, or propagates unsafe output into business workflows.

NHI Mgmt Group analysis

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.

Tool-using models create a verification trust gap. Traditional application security assumes a request either succeeds or fails inside a bounded workflow. Agentic AI breaks that assumption because the model can negotiate, retry, and delegate across multiple systems before a harmful action becomes visible. That is where identity governance becomes central: the question is not just what the model says, but which identities, secrets, and scopes it can invoke. Practitioners should treat every connected tool as an entitlement boundary.

Prompt injection is only the entry point, not the governance failure. The deeper issue is that organisations often test prompts while leaving tool permissions, data access, and output consumption controls under-governed. That creates a false sense of safety when the model appears well-behaved in isolation but remains dangerous in production. For identity and security teams, the lesson is to validate delegated access and downstream action paths as part of the security model. Practitioners should assume a model that can act has privileged reach until proven otherwise.

Continuous AI red teaming will increasingly separate audit readiness from operational maturity. The article’s emphasis on repeated testing, regression checks, and mapped frameworks reflects where the market is heading. AI systems change too quickly for one-off assessments to remain credible. Governance programmes will need evidence that security controls still hold after model updates, prompt changes, and new integrations. Practitioners should build recurring testing into release and access-change processes, not as a retrospective activity.

What this signals

Excessive agency is becoming the control gap that most teams under-estimate. Once models can invoke tools and act across workflows, the main risk is no longer inaccurate text but delegated action without sufficient constraint. That shifts programme priorities toward scoped permissions, auditability, and continuous control testing rather than isolated prompt hardening. For identity teams, the lesson is that AI agents need entitlement governance just as much as human users and service accounts do.

AI red teaming will increasingly need to sit next to identity governance and release management. If an AI system changes prompt templates, connectors, or access scopes without retesting, old assumptions collapse quickly. Organisations should expect red team evidence to feed directly into change approval, access review, and exception handling. That is the practical bridge between AI security testing and operational control.

The same pattern is visible in the rise of AI agents with hidden or poorly governed access. According to our research on AI agents, 80% of organisations say their agents have already acted beyond intended scope, which means governance is lagging deployment. Teams should treat that as a warning that continuous validation, not one-off assessment, is now the baseline.

For practitioners

• Define test scope around the full AI execution chain Include prompts, retrieval layers, connected tools, agent handoffs, and downstream systems that consume model output. Scope tests by the actual paths a model can take, not by the model alone.
• 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. That makes remediation easier to track across engineering and governance teams.
• 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. If an agent can call production tools, treat that path as privileged access and apply the same scrutiny used for PAM.
• Run continuous regression tests after every model or prompt change Re-test known jailbreaks, injection patterns, and data leakage scenarios whenever prompts, models, connectors, or retrieval sources change. A one-time red team exercise is not enough for systems that evolve continuously.
• Record findings in governance-ready evidence Preserve attack traces, failed attempts, reproducibility notes, and severity ratings so teams can make risk decisions and auditors can verify control effectiveness. Evidence quality matters as much as exploit discovery.

Key takeaways

• Gen AI red teaming is now a governance discipline because modern AI failures emerge across prompts, tools, retrieval, and delegated action paths.
• The strongest signal in the article is that agentic systems expand attacker impact once permissions, secrets, and API access are in play.
• Teams should operationalise continuous testing, risk taxonomy mapping, and scoped access review if they want AI security controls to hold after deployment.

Key terms

• AI Red Teaming: AI red teaming is the practice of simulating hostile behaviour against models, applications, and agents to expose weaknesses before real attackers do. In AI programmes, it is most useful when results can be turned into controls, monitoring, and governance evidence rather than left as a one-time test report.
• Excessive agency: A condition where an AI system is given more operational authority than its task requires. The risk is not just poor output. It is that mistakes, manipulation, or compromise can produce destructive actions at machine speed across the systems the agent can reach.
• Prompt Injection (Agentic): An attack where malicious instructions are embedded in content that an AI agent reads — causing the agent to execute unintended actions using its own legitimate credentials. A primary vector for agent goal hijacking and identity abuse.
• Retrieval-augmented Generation: Retrieval-augmented generation is a pattern where an AI model pulls external information before generating output. The security challenge is that access rules can weaken when data is chunked, embedded, cached, or reused, so source permissions may not automatically follow the content into the model's context.

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.

👉 Akto's full blog covers the red team workflow, risk mapping, and mitigation examples in more operational detail.

Deepen your knowledge

The NHI Foundation Level course, the industry's only accredited NHI security programme, covers NHI governance, agentic AI identity, and secrets management. It gives practitioners a structured way to connect identity controls to the AI systems and delegated access paths they now have to govern.