Gen AI red teaming is becoming essential for AI governance

At a glance

What this is: Akto argues that gen AI red teaming is a behavioural security discipline for finding prompt injection, jailbreaks, data leakage, poisoning and excessive agency before deployment.

Why it matters: This matters because AI governance now has to account for autonomous tool use, probabilistic failures and cross-system impact, which conventional application testing does not fully expose.

By the numbers:

• Akto says it automates 1000's of attack simulations across RAG pipelines, AI applications and prompts to identify risks before attacks can exploit them.

Context

Gen AI red teaming is the practice of actively probing AI systems for harmful behaviour, unsafe outputs and control bypass conditions. The governance gap is that large language models and agentic systems do not fail like ordinary software, so code review and infrastructure testing leave important behavioural risks untested.

The article is really about how AI security moves from static artefact checking to runtime behaviour testing across prompts, tools, APIs and multi-agent workflows. That intersection matters to IAM and NHI practitioners because agentic systems can invoke credentials, tools and downstream actions in ways that resemble non-human identity misuse even when the underlying model appears healthy.

Key questions

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. The failure is not usually a software crash. It is an apparently normal AI action that crosses a policy boundary, exposes information, or triggers an unsafe workflow. That is why pre-deployment red teaming is a governance control, not a nice-to-have test.

Q: Why do agentic AI systems create more security risk than standard chatbots?

A: Agentic systems can turn model output into action, which means a bad instruction can affect code flow, tool use, and downstream state. Standard chatbots usually stop at text generation. Agentic AI adds execution authority, so compromise can lead to disclosure, unauthorized changes, or misuse of credentials.

Q: What are the signs that AI red teaming is too shallow?

A: The usual signs are narrow test cases, no coverage of live workflows, no retesting after model or prompt changes, and findings that are not linked to remediation. If the programme only checks model accuracy or one-off demo prompts, it is not exercising the actual attack surface the business depends on.

Q: How should security teams run AI red teaming for GenAI systems?

A: Start with the system’s trust boundaries, then test prompts, retrieval sources, tool calls, and output handling together. Good AI red teaming does not stop at finding bad answers. It checks whether the system can be pushed into revealing data, bypassing policy, or taking unsafe actions through connected interfaces.

Technical breakdown

Why behavioural testing is necessary for generative AI

Generative AI systems are probabilistic, which means the same prompt can produce different outputs, different tool calls and different downstream effects across repeated runs. Traditional testing assumes more stable input-output behaviour and is therefore weak at surfacing jailbreaks, prompt injection and unsafe chaining across RAG pipelines or agentic workflows. Red teaming treats the system as a dynamic control plane rather than a static application, so the test target includes prompts, policies, retrieval layers, tools and orchestration logic. This is why attack simulation belongs in AI governance, not only in appsec reviews.

Practical implication: test the behaviour of the full AI workflow, not just the model or code path.

Prompt injection, jailbreaks and sensitive data leakage

Prompt injection and jailbreaks work by altering the model’s instruction hierarchy so the system follows attacker-controlled content instead of its intended policy. In RAG or tool-using systems, that can lead to disclosure of system prompts, API keys, proprietary content or other sensitive data embedded in context or responses. The article also points to hallucinations and misinformation as governance issues, because false but plausible outputs can corrupt business decisions even when no direct exfiltration occurs. The control problem is not only content safety, but also preventing model outputs from becoming an unauthorised data release path.

Practical implication: red team both disclosure paths and output-handling paths that can turn model responses into data leaks.

Excessive agency and tool misuse in agentic systems

Agentic systems expand the attack surface because the model can take actions, not just generate text. Once a model is allowed to call tools, APIs or multi-agent pipelines, a successful exploit can trigger real-world side effects such as unauthorised file operations, unsafe API calls or business workflow manipulation. That is where AI security starts to resemble NHI governance: permissions, scope, and delegated authority matter as much as the prompt itself. Red teaming therefore has to test whether the agent can be pushed beyond its intended role boundary and whether downstream systems trust its actions too readily.

Practical implication: treat tool permissions and delegated action scope as first-class security controls, not implementation details.

Threat narrative

Attacker objective: The attacker aims to make the AI system disclose sensitive information or perform unauthorised actions that affect business systems and decisions.

1. Entry begins with adversarial prompts, poisoned context or maliciously crafted inputs that reach the model or retrieval layer.
2. Credential access or control abuse follows when the system reveals prompts, sensitive data or tool-facing instructions that expand what the attacker can influence.
3. Escalation occurs when an agentic workflow accepts the model’s output as an authorised instruction and executes tool calls, API actions or workflow changes.
4. Impact is realised through data leakage, corrupted decisions, unsafe automated actions or broader compromise of the AI-enabled business process.

Breaches seen in the wild

• Meta Muse agent hijack 2026: An undocumented Muse setting let local malware hijack Meta's personal AI agent, steal its authentication material and abuse user access.

Read and download The State of NHI & AI Agent Breach Report 2026, covering 200+ breaches impacting Non-Human Identities including AI Agents.

NHI Mgmt Group analysis

Behavioural security is now a governance requirement for AI. Static testing can verify code, but it cannot reliably expose how a model will behave under adversarial prompting, retrieval poisoning or tool chaining. That makes red teaming a control for runtime trust, not a niche assurance exercise. Organisations that treat it as optional will understate their operational risk exposure, especially once agentic workflows can act on their behalf.

Gen AI red teaming surfaces a new control gap: delegated authority without behavioural assurance. AI systems can appear safe in isolated tests and still fail when prompts, context windows and tool permissions interact. The article’s strongest implication is that governance must move from model approval to end-to-end execution assurance. Practitioners should recognise that the failure mode is not only unsafe output, but unsafe action.

AI agent identity and NHI governance are converging. Once an agent can call APIs, access data or trigger workflows, it behaves like a non-human identity with delegated privilege and a distinct trust boundary. Agent action scope: the real risk is not simply that the model is intelligent, but that it can inherit authority faster than governance teams can certify it. IAM and NHI teams should therefore classify agent permissions, not just model access, as a governance object.

OWASP LLM and agentic AI risks are practical test targets, not abstract taxonomies. Mapping probes to prompt injection, sensitive information disclosure, data poisoning and excessive agency gives security teams a repeatable way to prioritise assurance work. The value is not the framework label itself but the ability to tie each probe to a specific failure mode. Practitioners should use the taxonomy to organise testing, remediation and audit evidence.

Continuous red teaming will outlast one-time assurance programmes. The article’s case studies and workflow examples show that models regress after updates, data changes and tool integration changes. That means AI governance needs recurring validation cycles and not just pre-production sign-off. Security teams should expect behaviour to drift and should design controls that assume drift will happen.

From our research library:

• AI-related credential leaks surged 81.5% year-over-year in 2025, with the surrounding AI infrastructure leaking 5x faster than core LLM providers, according to the State of Secrets Sprawl 2026.
• Gartner predicts that AI systems will initiate 50% of all service requests by 2030, driven largely by agentic AI.
• Read next: Agentic AI Security Guide

What this signals

Behavioural assurance will become the default test layer for agentic AI. As organisations move from copilots to systems that can call tools and execute workflows, the security question shifts from whether a model is accurate to whether it can be trusted with delegated action. The practical signal for practitioners is that AI governance now needs runtime validation, not just policy review.

Agent action scope is the new control boundary. Once a model can interact with APIs, data stores and multi-agent pipelines, it starts to resemble a non-human identity with real authority. Teams should prepare to govern that scope the same way they would govern privileged machine identities: by constraining access, logging behaviour and retesting after every material change.

For practitioners

• Define red-team scope around real AI failure modes Test prompt injection, jailbreaks, sensitive data leakage, poisoning and excessive agency as separate scenarios so findings can be prioritised and tracked.
• Instrument the full AI workflow for evidence Log prompts, retrieval results, tool calls, API actions and failed attempts so you can reconstruct how a behaviour emerged across the chain.
• Constrain agent permissions and tool access Limit what an AI agent can invoke, read and modify so a successful prompt exploit does not automatically become a business action.
• Bake regression red teaming into change control Retest after model updates, prompt changes, new data sources and new tool integrations because AI behaviour can drift after each change.
• Map findings to governance frameworks Use OWASP LLM and agentic AI mappings, plus NIST AI RMF and MITRE ATLAS where relevant, to turn test results into audit-ready evidence.

Key takeaways

• Gen AI red teaming is about exposing behavioural failure modes that static code and infrastructure testing do not catch, especially when models can call tools and APIs.
• The article shows that prompt injection, jailbreaks, poisoning and leakage are governance issues as much as technical issues because they affect trust, action and data exposure.
• Practical defence depends on continuous behavioural testing, scoped permissions and regression checks after every model, prompt or data change.

Key terms

• Automated AI Red Teaming: Automated AI red teaming is the use of software-driven tests to probe an AI system for weaknesses, unsafe behavior, and policy bypasses. It simulates adversarial prompts, tool misuse, data leakage, and agent manipulation at scale, then records failures so defenders can harden controls, monitoring, and response procedures.
• 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.
• 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.
• 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.

Deepen your knowledge

NHI Foundation Level course, the industry's only accredited NHI security programme, covers NHI governance, agentic AI identity, machine identity security and secrets management. It is designed for practitioners who need to govern delegated access, runtime authority and identity risk across modern security programmes.