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Use penetration testing to find technical vulnerabilities in a defined AI application’s infrastructure, APIs, integrations, and deployment. Use AI red teaming to see whether realistic attacks can make the model or an agent break its safeguards, expose information, or take actions without authorization. For an internet-facing AI system, an agent with tools, or an application handling sensitive data, plan for both: they test different parts of the risk.
What is the difference between AI red teaming and penetration testing?
The main difference is the question each exercise is designed to answer. A penetration test asks what technical vulnerabilities exist in a defined target. An AI red-team exercise asks whether an adversary can achieve a harmful or unauthorized outcome by exploiting how the AI system behaves, including how it interacts with data, tools, permissions, and people.
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OWASP describes penetration testing as scoped, time-boxed testing focused on technical vulnerabilities in a defined target. It characterizes red teaming as broader and more adversarial in intent. NIST’s definition of AI red teaming likewise emphasizes a structured effort, often using adversarial methods, to find flaws and vulnerabilities—including unforeseen or undesirable system behavior and risks from misuse.
These definitions overlap: a red-team exercise can uncover a technical vulnerability, and a penetration test can include an AI application’s exposed components. But the exercises are not interchangeable. One is organized around finding vulnerabilities within an agreed boundary; the other is organized around testing whether an adversary can reach a consequential objective.
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How the two exercises differ
| Dimension | Penetration testing | AI red teaming |
|---|---|---|
| Primary question | What technical vulnerabilities exist in the defined target? | Can a realistic adversary cause harmful, unsafe, or unauthorized behavior? |
| Typical scope | An application, API, cloud environment, network, integrations, or supporting infrastructure | Model behavior and, as relevant, prompts, retrieval, tools, permissions, people, processes, and attack chains |
| Approach | Structured, scoped, time-boxed vulnerability testing | Adversary emulation using objective-driven scenarios, creative prompts, indirect injections, multi-turn attempts, and tool-abuse tests |
| Useful output | Findings with severity, supporting evidence, and remediation guidance | An attack-path narrative, whether the objective was achieved or blocked, detection and response performance, blast radius, and control recommendations |
| Best suited to | Baseline technical assurance, a remediation backlog, and recurring technical testing | Validating safeguards, monitoring, authorization, and incident response under realistic abuse |
The distinction is especially important for an AI agent. A conventional security weakness may let someone reach an API or retrieve data; a model-behavior weakness may cause an otherwise authorized agent to misuse a tool after following hostile instructions. An assessment needs to consider both the technical route and the behavior that route can trigger.
Why an AI application’s risk crosses the model boundary
An AI feature is rarely just a model endpoint. The application may combine user prompts, a system prompt, retrieved documents, tool results, permissions, and actions performed through connected services. A model can respond in ways that are unsafe or unintended, while weaknesses in the surrounding application can expose or amplify that behavior. Testing only the model’s answers, or only the web application’s conventional vulnerabilities, can leave a gap.
OWASP guidance treats the model, prompts, retrieval pipeline, tools, and permissions as relevant parts of the AI and LLM application attack surface. That is a useful scoping principle: follow the path from an input to the information or action it could influence. For example, a red-team scenario might test whether hostile text in a retrieved document changes an agent’s behavior, whether a tool call is appropriately restricted, and whether any resulting disclosure is detected. This is a scenario to assess, not an assumption that every system is vulnerable.
The impact depends on the actual design. A chatbot that only returns text presents a different action surface from an agent that can read private records, send messages, or modify resources. The test plan should identify what the system can access and do, which users or tenants it serves, and what would count as an unacceptable outcome.
When should you choose penetration testing?
Start with penetration testing when the system boundary is clear and the main need is a conventional inventory of technical vulnerabilities. It is a good fit for checking the application and its supporting controls, especially when the organization needs findings it can assign, prioritize, fix, and retest.
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For an AI application, define whether the target includes its public interface, APIs, cloud environment, network, integrations, or other infrastructure. State which environments and accounts are in scope, what kinds of testing are permitted, and which systems must not be touched. A clear boundary helps the assessor test the intended target without treating the presence of an AI feature as permission to explore unrelated systems.
Penetration testing is particularly useful when you need to answer questions such as whether an API or integration has a technical weakness, whether an exposed component can be reached in an unintended way, or which findings need remediation first. It may identify weaknesses that enable an AI-specific attack, but a technical finding by itself does not establish how the model will behave when faced with adversarial input.
When should you choose AI red teaming?
Choose AI red teaming when the biggest uncertainty is the system’s behavior under misuse or adversarial pressure. It is especially relevant when users can supply untrusted content, the model retrieves external or private information, the application uses tools, or the system’s output can influence consequential decisions or actions.
Unlike a checklist limited to known technical weaknesses, an objective-driven red-team exercise explores whether combinations of inputs and system behavior can produce an undesirable result. The objective should be specific enough to judge success—for example, whether an attacker can cause unauthorized disclosure or an action beyond the user’s permission—without assuming the outcome in advance.
Red teaming also gives an organization a way to examine defenses beyond prevention. If an attack attempt is blocked, the team can still assess whether it was logged, detected, escalated, and contained as intended. A blocked attempt and an unobserved attempt have different implications for security operations, even when neither achieves the attacker’s objective.
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Do you need both?
For an AI system exposed to the internet or handling sensitive data, both are usually the more complete choice. Penetration testing examines technical controls around the system; AI red teaming probes model behavior and realistic attack chains. One does not replace the other.
If budget or time requires staging the work, choose the first exercise according to the main uncertainty:
- Choose penetration testing first when the target and acceptance criteria are clear and you need a technical vulnerability inventory.
- Choose AI red teaming first when the primary concern is prompt injection, safety-policy bypass, misuse, agent permissions, or consequential tool use.
- Coordinate both when a system combines exposed infrastructure or sensitive data with model-driven behavior. Agree how findings can be connected so a technical weakness and the behavior it enables are not reported as unrelated issues.
The right order is risk-dependent, not a universal rule. A technical assessment can establish weaknesses in the surrounding environment before a red-team exercise explores their consequences. In another case, uncertainty about an agent’s authority or response to untrusted inputs may justify testing its behavior first. Whatever the order, scope each activity so its findings answer a distinct question.
What should an AI red-team assessment cover?
Build scenarios around the system’s actual inputs, data flows, permissions, and actions. OWASP’s AI and LLM application guidance identifies several attack areas worth considering. Not every area applies to every design, so map each one to a component and a plausible impact rather than treating the list as a claim that every system has the same exposure.
Prompt injection and jailbreak attempts
- Test direct prompt injection: user-supplied instructions intended to override or undermine the system’s constraints.
- Test indirect prompt injection: malicious instructions embedded in documents, web pages, emails, or tool results that the system may retrieve or process.
- Probe jailbreak attempts involving role-play, encoded inputs, persona manipulation, or escalation across multiple turns.
Include the route by which the content reaches the model. A test that only enters text into a chat box will not establish how the application handles hostile instructions in retrieved material or tool output.
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Information disclosure
Test whether the system can expose personal information, secrets, another tenant’s data, system prompts, conversation history, or retrieved context to an unauthorized user. Record what data was accessible in the test environment and the path by which it was reached. Avoid treating disclosure of one kind of content as proof that every data store or user is exposed.
Output handling and tools
- Check whether model output is passed to a browser, shell, SQL engine, template system, or another interpreter without appropriate safeguards.
- Assess whether injected instructions can make an agent call tools, send data, or modify resources beyond the user’s authorization.
- Consider tool-output injection, data-exfiltration channels, sandbox escapes, and attack chains involving multiple agents where those features exist.
For an agent, list its available tools and the permissions attached to each. Then define which actions are allowed for the tested user and scenario. This makes it possible to distinguish a risky-looking response from an actual unauthorized action, and to report precisely how far an attack progressed.
Retrieval and resource consumption
- Examine vector and embedding-related weaknesses, including poisoning risks and failures to enforce retrieval authorization.
- Consider unbounded consumption, such as token floods, recursive loops, expensive tool calls, denial of service, or runaway cost.
Scope these tests with safeguards appropriate to the environment. Resource-exhaustion testing can affect availability or incur expense; an assessment should specify what is permitted, how impact will be limited, and who can stop the test.
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A useful engagement begins with a decision about what the organization needs to learn, not with a generic request to “test the AI.” Define the system and the outcomes that matter, then select the technical and behavioral methods that can evaluate them.
- Describe the system and boundary. Identify the model-backed features, APIs, deployment environment, integrations, retrieval sources, tools, permissions, user groups, and data sensitivity relevant to the test. Clarify which components are in scope and which are excluded.
- Set objectives and safety limits. Define the harmful or unauthorized outcomes to test, the environments and accounts that may be used, permitted test activity, and any limits on data access, resource use, or changes to real resources. Name the contact who can pause testing if needed.
- Choose the assessment mix. Use penetration testing for technical vulnerabilities in the defined target, red teaming for adversarial behavior and attack objectives, or both when the system’s risk spans both. Use the OWASP Top 10 for LLM Applications as a planning checklist and MITRE ATLAS as a catalogue for mapping adversary techniques.
- Agree on evidence and reporting. Require the assessor to record test conditions, prompt or payload classes, affected components, attack path, impact, and reproducibility. For a red-team exercise, also agree how objective achievement, detection, escalation, containment, and blast radius will be described.
- Assign remediation and retest. Connect each finding to an owner and a corrective action. Retest material fixes using the relevant method; a code or configuration fix may need technical retesting, while a change to prompts, permissions, or tools may require a behavioral scenario to be repeated.
This plan keeps a red-team report from becoming a collection of striking prompts without evidence of impact, and keeps a penetration-test report from implying that an application is safe from AI-specific misuse simply because its conventional technical findings were addressed.
What should the report measure?
For penetration testing, require findings that include severity, evidence, affected components, and remediation guidance. The report should make clear what was tested and under what conditions so the organization can reproduce and prioritize the issue.
For red teaming, a finding should explain the scenario and attack path, whether the objective was achieved, and how far the attempt progressed. It should also document detection time, containment, human escalation, and the potential blast radius. These details help separate a blocked attempt from a successful exploit and reveal where response controls need improvement.
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NIST identifies measures for tracking red-team activity, anomalous events, downtime, incident-response time, and time-to-bypass. These are improvement metrics, not a universal score or pass/fail threshold. Interpret them against the exercise objectives, system design, and the organization’s own risk tolerance; a single number cannot substitute for the evidence behind an attack path.
How often should you repeat testing?
Repeat targeted red-team exercises after material changes to the model, system prompt, retrieval corpus, tools, permissions, or deployment context. Each of these changes can alter how an attack reaches the system or what the system can do, so reuse relevant scenarios and update them for the changed design.
Continue technical penetration testing according to the organization’s risk and assurance cycle. The appropriate cadence depends on the system and its exposure; the available guidance does not establish a universal interval. A change-driven red-team exercise and recurring technical testing serve related but different purposes.
What standards and methods should guide the work?
OWASP recommends using the OWASP Top 10 for LLM Applications as a planning checklist and MITRE ATLAS as an adversary-technique catalogue for mapping findings. These resources can help structure coverage and describe observed techniques, but neither makes a test complete by itself. The plan still needs to reflect the particular application’s architecture, objectives, data, and permissions.
ISO/IEC AWI TS 42119-7 is an approved work item under development. Its stated scope includes AI red-teaming terminology, risk and attack-vector identification, planning and execution methodology, reporting, and integration into the AI lifecycle. It is not yet a published final standard, so it should not be presented as a finalized requirement.
The UK NCSC distinguishes adversary simulation—replicating likely adversary actions to achieve specified consequential outcomes—from penetration testing, which is more focused on identifying technical vulnerabilities. That distinction reinforces the practical choice: decide whether the engagement is primarily meant to find weaknesses in a defined target or to test whether an adversary can achieve a meaningful outcome.
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Common mistakes that weaken the result
- Calling every AI security exercise a penetration test. That can obscure whether anyone tested model behavior, prompt injection, or unauthorized tool use.
- Treating red teaming as a replacement for technical testing. A realistic adversarial exercise does not automatically inventory the application’s infrastructure, API, and integration vulnerabilities.
- Testing prompts without following the attack path. A surprising answer is not equivalent to a verified disclosure or unauthorized action; document the affected component and impact.
- Ignoring permissions and retrieval boundaries. Without knowing what the system should be allowed to access or change, the assessor cannot reliably judge whether an outcome is unauthorized.
- Accepting a report without reproducible evidence. Record test conditions, payload classes, affected components, and the steps needed to understand or reproduce findings.
- Assuming a standard defines a universal engagement. Scope and methods must reflect architecture, permissions, data sensitivity, and objectives; the cited guidance does not establish a universal price, duration, certification requirement, or compliance mandate.
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