Launching an AI-powered application without thorough security testing is like deploying code into production without reviewing a single line. In cybersecurity, that's not just risky, it's reckless.
This is where AI red teaming becomes essential. It simulates realistic attacks against an AI application to identify weaknesses before someone else does. It complements traditional penetration testing by focusing on risks specific to AI systems, like prompt injection, jailbreaks, and system prompt leakage.
A strange response from the model is just the surface. What matters is where the application's intended boundaries actually break down, what an attacker could gain, and which controls need to change.
Why AI Red Teaming Must Test the Full Application
Testing a few variations of "ignore your previous instructions" is not a red teaming strategy.
An AI application may reject an obvious jailbreak while still following instructions hidden inside a webpage, exposing information from a retrieval system, or passing unsafe output into another application. An agent could generate a harmless response while quietly calling the wrong tool or taking an action it was never authorized to take.
That is why effective red teaming looks beyond the model to test:
- System and developer instructions
- Retrieval sources and external content
- Sensitive data and access boundaries
- Output handling and downstream applications
- Tools, APIs, memory, and agent permissions
- Multi-step actions across connected systems
- Token, compute, and resource consumption
The more access an AI system has, the more important this becomes. A chatbot that only answers questions has a different risk profile from an agent that can read email, update customer records, execute code, or initiate a transaction.
A large-scale competition analyzed by NIST illustrates the challenge. More than 400 participants conducted over 250,000 attacks against 13 frontier models in tool-use, coding, and computer-use scenarios. At least one successful attack was found against every model, and stronger model capabilities did not consistently mean stronger security.
A refusal is not proof of security. It is one result from one test against one version of the system.
How AI Red Teaming Works
AI red teaming follows a structured process designed to reflect how a real attacker might approach the application, scoped enough to run in weeks, not months.
1. Threat Modeling
Start by identifying what the system can access, what it is allowed to do, and what would matter most to an attacker.
This includes obvious targets such as sensitive data and system prompts, but also tools, credentials, retrieval sources, memory, and downstream actions.
The threat model should account for:
- External users trying to bypass restrictions
- Insiders attempting to access information outside their role
- Malicious instructions embedded in third-party content
- Attackers manipulating an agent's tools, goals, or memory
- Cross-user or cross-tenant data leakage
- Theft or misuse of credentials available to the model or its tools
- Unauthorized actions caused by excessive agency or weak approval controls
- Compromise of models, plugins, dependencies, or other supply-chain components
- Abuse that creates excessive cost or disrupts availability
The application's business purpose matters. The likely attacks against a customer support chatbot will not be the same as those targeting an autonomous coding agent.
2. Scenario Building
Turn those threats into realistic test cases.
A generic prompt library can provide a useful baseline, but the strongest scenarios reflect how the application is actually used. Examples might include:
- A user trying to retrieve another customer's records
- A malicious instruction hidden inside a document the system is asked to summarize
- A user attempting to reconstruct the system prompt through repeated, incremental questioning
- An agent stuck in a repeated tool-calling loop from a single crafted request
- Several individually permitted actions chained into an unauthorized result
The goal is to test plausible abuse, not simply collect creative prompts.
3. Adversarial Testing
Testers execute the scenarios using a combination of manual techniques and automation.
Automated testing can run large prompt sets, generate variations, test multiple languages and formats, repeat attacks, and compare results across application versions. Human testers can adapt based on the system's responses, recognize partial successes, and combine smaller weaknesses into a more serious attack chain.
For example, a tester may first identify a small information leak, use it to infer internal instructions, and then exploit that knowledge to manipulate a tool or access restricted data.
Useful techniques include:
- Direct and indirect prompt injection
- Jailbreak, persona, and role-play attacks
- Multi-turn attacks that gradually weaken or bypass controls
- Chaining individually permitted requests into a prohibited outcome
- Encoding, translation, obfuscation, and other content transformations
- Manipulation of JSON, code, markdown, and other output formats
- Poisoning of retrieved documents, memory, or external content
- Abuse of tools, credentials, permissions, and approval workflows
- Repeated probing across users, sessions, languages, models, and application contexts
Automated testing provides coverage, consistency, and scale. Human testing provides adaptability, creativity, and an understanding of business impact. Effective AI red teaming requires both.
4. Analysis and Remediation
A successful attack needs to be evaluated in context.
A persistent jailbreak that changes the assistant's tone may be less important than an attack that works less often but exposes customer data, sends an email, executes code, or initiates a transaction. Rank findings by what the attack made possible, not by how often they land or how impressive they look.
Findings should capture:
- What happened
- How it was reproduced
- Which data, tools, or permissions were involved
- How reliably the attack worked
- The potential business impact
- Which control failed
- What needs to change
Fix the control at the layer where the failure occurred: tighten access, reduce tool permissions, validate retrieved content, sanitize outputs, require human confirmation, add resource limits, or adjust system instructions. Then rerun the original attack and nearby variations, not just the exact prompt that triggered it.
A report is just a record of what broke. The value comes from fixing it and testing again.
Lessons From Red Teaming a Government Education Chatbot
That process looks different once it's pointed at something real.
Prompt Security researchers red teamed a government office chatbot for resident education questions. The bot held up well against standard jailbreaks and role-play attempts, rejecting them on intent rather than keyword matching.
It broke once testers stopped varying what they asked for and started varying the format instead. JSON-wrapped requests and Base64-encoded output each slipped past filters built to catch meaning rather than structure on their own, and chaining those tricks with a fabricated debug-mode request eventually extracted a near-verbatim copy of its own system prompt.
The lesson generalizes past this one bot: a program that only varies prompt wording will miss an entire category of attack.
When to Retest
An AI red team assessment applies to a specific version of the application. Change the system, and the result may no longer hold.
Targeted tests should be rerun after meaningful changes to:
- The underlying model
- System or developer instructions
- Retrieval sources
- Connected tools or APIs
- Agent permissions
- Input or output controls
- Application workflows
- Human approval requirements
Testing should also be repeated when a new attack technique appears or production monitoring reveals unexpected behavior.
Continuous red teaming does not mean running every attack all the time. It means maintaining reusable test cases, rerunning the relevant ones after changes, and adding new scenarios as the application and threat landscape develop.
Tools and Frameworks
Security teams do not have to build every test or methodology from scratch.
The OWASP Top 10 for LLM and generative AI applications provides a useful baseline for risks such as prompt injection, sensitive information disclosure, poisoning, improper output handling, excessive agency, system prompt leakage, and unbounded consumption.
The NIST AI Risk Management Framework can help connect technical testing to broader risk management and governance.
The OWASP Top 10 for Agentic Applications extends that baseline to risks specific to autonomous agents: tool misuse, identity abuse, memory poisoning, and cascading failures across multi-agent systems.
MITRE ATLAS provides a knowledge base of tactics and techniques associated with attacks against AI-enabled systems, while Microsoft PyRIT offers an open-source framework for organizing and automating generative AI risk testing.
These resources are starting points. They cannot replace threat modeling based on the application's actual data, users, tools, and permissions.
What AI Red Teaming Can and Cannot Tell You
AI red teaming can uncover vulnerabilities, demonstrate realistic attack paths, and show where an application's controls fail under pressure.
It cannot prove that an AI system is completely secure.
Every assessment covers a finite set of scenarios against a particular configuration. Unknown techniques, new integrations, model changes, system prompt changes, and production conditions can introduce failures that were not present during testing.
Red teaming should therefore operate alongside secure application design, least-privilege access, runtime monitoring, logging, incident response, and change management.
A pile of successful jailbreaks doesn't tell you much on its own. What matters is finding where the application's intended boundaries break down, applying the right controls, and verifying they hold.
As AI systems gain access to more data, tools, and permissions, the question is no longer only whether a model can be made to say something it should not.
It is whether the application can be made to do something it was never authorized to do.
