Most security testing programs are built around a target that behaves the same way every time someone attacks it, a network, an API, a fixed list of known vulnerability classes. A large language model doesn’t hold still like that. Ask it the same manipulative prompt twice and you can get a clean refusal once and a leaked answer the next time, which is exactly why AI red teaming has become its own discipline instead of a line item inside a normal penetration test.
Most testing programs are built to catch what repeats. AI systems don’t fail the same way twice, and that’s usually where the real exposure hides.
Whoever ends up owning that question, security, product, or the team that just shipped an internal copilot, the answer is usually the same: nobody has actually tried to break it on Worth separating this from a related idea you’ll often see sitting right next to it. Some teams focus on continuously watching what AI systems exist, what data each one touches, and how exposed they are day to day. AI red teaming is different: an active attempt to break one specific system on one specific day. Ongoing visibility tells you where the risk sits. Red teaming tells you how bad it gets when someone actually tries to exploit it. That visibility work feeds into what a red team should prioritize testing, but the two are not interchangeable, and treating one as a substitute for the other leaves a real gap.
What AI red teaming actually tests
Early AI red teaming stopped at the chatbot. Can you get it to say something offensive, something violent, something legal would flag. That’s still part of it, but it’s the shallow end.
A real AI red teaming exercise works across four levels. At the model level, testers probe for harmful or biased outputs, jailbreaks, and confidently wrong answers presented as fact. At the application level, they test prompt injection, both the direct kind where a user types something malicious, and the indirect kind where the attack hides inside a document, a webpage, or an email the AI is asked to summarize. At the system level, they test what happens once the AI has tools, memory, or the ability to call other services, because that’s where a clever prompt turns into an actual action. And at the data level, they test what the system can be tricked into revealing or acting on: customer records, credentials, internal documents it was never supposed to have an opinion about.
That fourth level gets skipped constantly. Most AI red teaming reports read like a content safety audit: did the bot say something embarrassing. Few ask whether the AI could be manipulated into pulling sensitive data out of a connected database or a retrieval pipeline and handing it to whoever asked nicely enough. That’s where the actual business risk sits, not “the bot was rude” but “the bot leaked something.”
AI red teaming vs pentesting and blue teaming
People conflate these three constantly, and the confusion is understandable since all three involve someone trying to break something on purpose.
Traditional pentesting assumes a mostly static target: a network, an app, a known set of vulnerability classes. Blue teaming is defense, the people watching for and responding to an attack already in progress. AI red teaming borrows the adversarial spirit of both but points it at a target that doesn’t behave the same way twice. Ask the same jailbreak prompt on Tuesday and Thursday and you might get two different answers, because the model is probabilistic, not because something got patched in between.
| Dimension | Traditional pentesting | Blue teaming | AI red teaming |
| Core question | Can this system be breached | Did we catch the attack in time | Can this AI be manipulated into unsafe or harmful behavior |
| Target | Networks, apps, infrastructure | Live production environment | Models, prompts, agents, and the data they can reach |
| Cadence | Point in time, often annual | Continuous, real time | Point in time today, moving toward continuous |
| Typical output | Vulnerability list with severity scores | Detection and response metrics | Failure modes, harmful outputs, exploitable prompt paths |
| Who usually runs it | Internal security or a third party firm | Internal security operations center | Mixed team: security, ML engineers, sometimes product |
A clean pentest report tells you the API is locked down. It says nothing about whether the model behind that API can be talked into doing something it shouldn’t. Treating a pentest as a substitute for AI red teaming, or the reverse, leaves whichever layer got skipped completely untested.
The three stages of AI red teaming maturity
Most organizations doing AI red teaming today sit in the same place: one exercise before a launch, a slide deck of findings, nothing touched since. That’s stage one. It counts for something, but it’s not the ending point.
Stage 1: ad hoc AI red teaming
One exercise, usually run before a launch or a major release. Scope is whatever the team remembers to test, often just obvious jailbreaks and offensive content. Findings land in a document that nobody revisits until the next launch prompts someone to ask if this has ever been done before.
Stage 2: structured AI red teaming
Scope gets defined and mapped to an actual framework, NIST AI 600-1 or the OWASP GenAI LLM Top 10 most often. Testing happens on a set cadence, quarterly or tied to model and prompt changes, rather than only before launch. Findings get assigned an owner and tracked to resolution instead of sitting in a slide deck.
Stage 3: continuous AI red teaming
Adversarial testing runs continuously, often with automated tooling doing the repetitive probing so human testers can spend their time on novel attack paths. Findings feed directly into runtime controls, not just a pre-launch checklist, so a technique that worked last month gets blocked automatically this month.
Jumping straight to stage 3 without stage 2 rarely works. The framework and the cadence need to exist before automation has anything meaningful to automate against.
AI red teaming techniques and methods your team should know
A few techniques show up in nearly every mature program.
Prompt injection: instructions hidden in a user message, a retrieved document, or a tool’s output that override the system’s intended behavior. Indirect prompt injection, where the malicious instruction sits inside content the AI reads rather than content a user typed, currently ranks as one of the highest-priority risks on the OWASP GenAI LLM Top 10.
Jailbreak elicitation: multi-turn conversations designed to walk a model past its own guardrails gradually, rather than asking directly for something it would normally refuse outright.
Data extraction probes: attempts to get a model or an agent to reveal training data, system prompts, or connected data sources it has access to but shouldn’t surface.
Data poisoning checks: testing whether an attacker could corrupt the data a model trains or fine-tunes on to change its behavior later. Relevant mostly for teams doing their own fine-tuning rather than calling a third-party API.
Automated adversarial testing: tools like PyRIT, Microsoft’s open source AI red teaming framework, run large batches of known attack patterns so human testers spend their time on attacks nobody has scripted yet.
None of these replace judgment. A tool can run ten thousand jailbreak variants overnight. It can’t tell you whether the one that worked actually matters to your business.
Why agentic AI red teaming needs a different playbook
Give an AI system the ability to call tools, hold memory across a session, or hand tasks to other agents, and the attack surface stops looking like a chatbot’s. The Cloud Security Alliance published a dedicated Agentic AI Red Teaming Guide in May 2025 because existing model-level and application-level testing didn’t cover what happens once an AI can act, not just answer.
The guide defines risk categories that don’t show up in a standard LLM red team. Permission escalation: an agent grants itself, or another agent, more access than intended. Memory manipulation: an attacker plants something in an agent’s persistent memory that shapes behavior in a later, unrelated session. Orchestration flaws: a multi-agent workflow gets redirected by compromising the weakest agent in the chain. Goal hijacking: a subtle instruction buried in a tool’s output redirects what the agent is actually trying to accomplish, without ever touching its original instructions.
This is also where the data risk gets sharpest. An agent with a database connector and a messaging integration doesn’t just risk saying something wrong. It risks actually moving sensitive data somewhere it shouldn’t go, on its own, following a chain of reasoning nobody directly authorized. Red teaming an agent means testing that chain end to end, not just the first prompt.
Once a red team exercise surfaces which agent actions should never complete without a human check, the harder problem is enforcing that in production, not just in a test report. Matters.AI’s Agentic AI remediation layer for agentic systems is built for that specific handoff, stepping in when an agent’s live action pattern matches something a red team already flagged as unsafe.
Building an AI red teaming program that doesn’t stall after one exercise
Scope comes first, and scope is where most programs quietly fail. You can’t red team what you don’t know exists. Before testing starts, you need an actual inventory: which AI systems are live, what data each one can reach, and which ones a team spun up without telling security.
That inventory problem is a data discovery problem wearing a different name. You can’t scope a red team exercise for data exposure risk if nobody can say what sensitive data a given AI system actually touches in the first place, which is exactly what Data Security Intelligence maps out before red teaming even starts, rather than leaving that mapping to guesswork.
With scope defined, three things separate a program that sticks from one that fades. A cadence tied to real triggers, a new model version or a new tool integration, not just a date on a calendar. A cross-functional team that includes someone who understands the product’s actual business logic and not only security. And a tracking system that assigns every finding to an owner with a deadline. Skip any one of these and the program reverts to stage one: a single exercise, a slide deck, silence until the next launch.
The frameworks anchoring AI red teaming right now
Four references show up in almost every serious AI red teaming program today.
NIST AI 600-1, the Generative AI Profile published in July 2024 as a companion to the base NIST AI Risk Management Framework, focuses on what to do with red team results once you have them, tying that evidence to its Measure function rather than prescribing how to run the attack itself.
The OWASP GenAI LLM Top 10, now in its 2026 edition after a run as the 2025 list, ranks the most critical risks specific to large language model applications, with prompt injection consistently near the top. It functions as the closest thing the industry has to a shared vocabulary for LLM-specific risk.
The Cloud Security Alliance’s Agentic AI Red Teaming Guide, published May 2025, is the reference to reach for the moment an AI system can take actions rather than just generate text.
MITRE ATLAS works like an AI-specific version of the ATT&CK framework, cataloging documented adversarial tactics and techniques against AI systems. Useful for grounding a threat model in real attack patterns rather than starting from a blank page.
None of these are mandatory today in most jurisdictions. Regulatory pressure toward required AI red teaming exists in some sectors and some regions and not others, and it’s shifting fast enough that a specific claim here would likely be outdated within months. What’s steady is that examiners and enterprise customers increasingly ask for red team evidence as proof of due diligence, mandate or not.
Making AI red teaming pay off past the first exercise
AI red teaming only earns its keep when findings change something. A report that confirms what everyone suspected and then sits in a shared drive isn’t a program, it’s an expensive way to feel informed. The organizations getting real value here treat every finding as a ticket, not a footnote.
Once you know which prompts, tools, and agent actions your red team has proven are unsafe, the next problem is catching them in production before they repeat, which is exactly what real-time AI interaction control watches for, flagging a live AI session the moment it matches a pattern your red team already proved was unsafe.
