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AI red teaming tests whether an AI system can be manipulated into unsafe, misleading, privacy-violating, or unauthorized behavior. Penetration testing tests whether an attacker can bypass technical security controls in the application, infrastructure, identity systems, or network around it. They overlap in adversarial intent but answer different questions and produce different evidence. An AI-enabled product normally needs both: red teaming for the model and AI workflow, and penetration testing for the conventional attack surface that hosts and contains it.
What AI red teaming is
NIST defines AI red teaming as a structured testing effort that often uses adversarial methods to find flaws and vulnerabilities in an AI system, including unforeseen or undesirable behavior and risks associated with misuse. The activity is usually conducted with the AI developers or product owners so testers can examine not only the model, but also the prompts, data, tools, policies, and runtime controls that shape its behavior.
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The system under test is larger than the model
A modern AI feature is a chain of components rather than a standalone language model. A useful scope can include:
- the base or fine-tuned model and its safety behavior;
- system prompts, developer instructions, policy filters, and conversation memory;
- retrieval-augmented generation (RAG), vector stores, embeddings, document parsers, and tenant boundaries;
- tools and APIs that let an agent read, write, send, purchase, execute, or administer;
- input and output transformations, including templates, browsers, shells, SQL engines, and downstream APIs;
- monitoring, rate limits, budget controls, human approvals, and incident response.
OWASP’s GenAI testing guidance organizes this work into model evaluation, implementation testing, infrastructure assessment, and runtime behavior analysis. That framing prevents a team from declaring an AI feature safe merely because the model refuses a handful of prohibited prompts.
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What penetration testing is
NIST SP 800-115 defines penetration testing as security testing in which assessors mimic real-world attacks to identify ways to circumvent the security features of an application, system, or network. Testers use reconnaissance, scanning, exploitation, and controlled post-exploitation to validate whether weaknesses can be combined into a meaningful attack path.
The target may be an internet-facing API, web application, mobile backend, cloud account, container, host, network segment, identity provider, storage service, or administrative interface. A penetration test can include an AI product, but its governing question remains technical exploitability: can an attacker gain unauthorized access, privilege, data, persistence, or impact by bypassing controls?
NIST cautions that this work is labor-intensive, requires expertise and planning, and can disrupt or damage systems if it is not carefully controlled. A written authorization, scope, emergency contact, test window, data-handling plan, and rules of engagement are therefore part of the security control, not paperwork added after the fact.
AI red teaming and penetration testing compared
| Axis | AI red teaming | Penetration testing |
|---|---|---|
| Primary target | Model behavior, prompts, training or retrieval data, safety controls, tools, agent logic, and runtime behavior | Applications, hosts, networks, identity, configurations, and conventional security controls |
| Central question | Can the AI be induced to produce unsafe behavior, disclose information, or take an action beyond the user’s authority? | Can an attacker bypass controls and compromise the system, data, or environment? |
| Typical techniques | Adversarial prompting, jailbreaks, indirect injection, poisoning, misuse scenarios, human creativity, and automated evaluations | Reconnaissance, scanning, exploitation, vulnerability chaining, privilege escalation, and controlled post-exploitation |
| Evidence | Reproducible prompts, conversations, model outputs, tool-call traces, affected policy or control, severity, and mitigation | Requests and responses, exploit steps, affected asset, gained access, impact, remediation, and retest evidence |
| Typical owners | AI safety or security, ML engineering, product, trust and safety, and governance teams | Application, infrastructure, network, identity, and security operations teams |
| Operational risk | May involve abusive prompts or production-like agent actions; use a controlled environment and explicit guardrails | May interrupt or damage systems because real attacks can be launched; use approvals and rules of engagement |
The tests are complementary, not competing substitutes. A model can resist jailbreaks while its API has broken authorization. Conversely, a perfectly patched API can still allow a malicious document to steer an agent into emailing confidential records.
What an AI red team can find
Prompt injection and jailbreaks
Direct prompt injection attempts to override instructions in the user’s message. A jailbreak may use role-play, encoding, multi-turn pressure, translation, or conflicting instructions to evade a safety policy. The important finding is not simply that a particular phrase produced an undesirable answer; it is whether the control fails reliably enough to create a safety, privacy, fraud, or security consequence.
Indirect prompt injection is often more consequential in connected systems. The hostile instruction can be hidden in a retrieved document, email, web page, calendar entry, image caption, or tool result. When the agent treats that content as an instruction rather than untrusted data, it may reveal context, alter a record, or call a tool without the user’s intent.
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Sensitive-information disclosure
Testing should look for leakage from every context boundary: the user’s prompt, conversation memory, retrieved documents, hidden instructions, logs exposed through an interface, and data used during training or fine-tuning. Red-teamers vary identities, tenants, roles, languages, and conversation history to determine whether one user can obtain another user’s information.
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Improper output handling
Model output becomes a security issue when another component interprets it as code, markup, a query, a command, a template, or an API request. Tests can place payloads in generated HTML, SQL, shell commands, JSON, URLs, spreadsheet formulas, or workflow fields and then observe whether the application escapes, validates, and constrains the result before execution.
This is an implementation failure as much as a model failure. A refusal policy cannot compensate for an application that inserts untrusted output directly into a browser, shell, SQL engine, template renderer, or privileged API.
Excessive agency and unauthorized tool use
Agentic systems may search internal sources, send messages, change tickets, modify files, approve transactions, or invoke infrastructure APIs. Red teaming tests whether an injected or ambiguous instruction can make the agent act outside the user’s authority, skip a required confirmation, access a broader dataset, or chain low-risk tools into a high-impact outcome.
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Evidence should include the full tool-call trace: the triggering content, arguments, identity or token used, approval state, resources changed, and rollback result. A model’s natural-language answer is not enough to establish what the system actually did.
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System-prompt leakage and false reliance on prompts
Attackers may try to extract system instructions, hidden policies, connector names, or internal routing details. Disclosure is not automatically a critical vulnerability, but it can expose sensitive design information or make later attacks easier. The larger issue is relying on a prompt as if it were an access-control boundary. A system prompt can guide behavior; it cannot replace server-side authorization, isolation, validation, or secret management.
Retrieval, vector, and embedding weaknesses
RAG pipelines introduce data and authorization risks that ordinary chatbot testing can miss. Red-team cases include poisoned documents that carry hidden instructions, malicious metadata, cross-tenant retrieval, embedding collisions, access filters applied after generation rather than before retrieval, and stale permissions that survive document changes.
The test should verify authorization at retrieval time and again at response time. It should also test deletion, re-indexing, duplicate content, document provenance, and whether citations or snippets reveal records the requesting user cannot open.
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Attackers can submit token floods, force recursive agent loops, trigger expensive tool calls, generate huge documents, or repeatedly invoke a high-cost model. These cases can cause denial of service, exhausted quotas, queue starvation, or unexpected spending even when no data is stolen.
Controls to examine include input and output limits, recursion depth, per-user and per-tenant budgets, timeout behavior, concurrency caps, circuit breakers, tool-call quotas, and alerts. The report should show which limit was absent or bypassed and what resource was consumed.
Broader adversarial machine-learning attacks
NIST AI 100-2e2025 describes a broader taxonomy covering evasion, poisoning, privacy, and misuse attacks against predictive and generative systems across learning methods and data modalities. Depending on the product, a red team may therefore examine adversarial inputs that evade a classifier, poisoned training or feedback data, membership or model-inversion risks, and abuse of a generative system for harmful tasks.
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Not every category applies to every deployment. The threat model should state which learning method, data modality, update process, and user population are in scope instead of treating the taxonomy as a checklist that must be completed indiscriminately.
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Exposed services and vulnerable components
Reconnaissance and scanning identify reachable hosts, ports, APIs, administrative panels, outdated libraries, insecure defaults, and cloud services that were unintentionally exposed. Exploitation validates whether a finding is actually reachable and useful to an attacker rather than merely present in a scanner report.
Authentication and authorization failures
Testers check password and session controls, multi-factor enforcement, token validation, account recovery, object-level authorization, role separation, and tenant isolation. In an AI application, this includes the endpoints that issue model responses, retrieve documents, execute tools, stream conversations, and export logs.
Application-logic and input-validation flaws
Business-logic tests look for ways to skip approval steps, replay requests, alter prices or quotas, access another user’s job, upload dangerous content, or bypass a workflow by changing parameters. Conventional injection, cross-site scripting, server-side request forgery, insecure deserialization, and file-handling flaws may also be relevant, depending on the technology.
Network, cloud, and privilege-escalation paths
A test can reveal weak segmentation between an AI service and databases, metadata endpoints reachable from a workload, overly broad cloud roles, exposed secrets, container escapes, or paths from a low-privilege account to an administrator. The meaningful result is the chained path and its impact, not the number of individual scanner findings.
Detection and response gaps
Although the primary objective is exploitability, a controlled engagement can show whether logging, alerting, blocking, and incident procedures recognize realistic attack activity. The report should separate prevention failures from detection delays and state what evidence defenders could or could not retrieve.
How the two engagements run
AI red-team workflow
- Set authorization and boundaries. Identify the model versions, environments, tenants, tools, data sources, prohibited actions, test accounts, rate limits, and emergency stop procedure.
- Build a threat model. Map users, adversaries, assets, trust boundaries, prompt and retrieval flows, model updates, tools, and possible harms. Include misuse cases as well as conventional confidentiality, integrity, and availability goals.
- Design test cases. Combine human-created scenarios with automated prompt mutations. Cover direct and indirect injection, disclosure, unsafe output handling, agency, retrieval, poisoning, privacy, and resource exhaustion where applicable.
- Run in a controlled environment. Use synthetic or minimized data, non-production credentials, sandboxed tools, approval gates, transaction limits, and monitoring. If production-like testing is essential, make the allowed actions and rollback plan explicit.
- Capture behavioral evidence. Preserve prompts, retrieved context, model versions, temperature or other relevant settings, outputs, tool calls, identities, timestamps, policy decisions, and resulting state changes.
- Triage and reproduce. Confirm the behavior with a second tester or repeatable harness, determine affected users and assets, and distinguish a one-off odd response from a reliable exploit path.
- Report and retest. Assign an owner, severity, root cause, mitigation, residual risk, and verification method. Retest the repaired control, including nearby variants rather than only the original prompt.
Penetration-test workflow
- Plan. Establish written permission, in-scope assets, exclusions, test dates, source addresses, credentials, data-handling rules, communication channels, and stop conditions.
- Discover. Gather information about domains, services, applications, APIs, identities, versions, and trust relationships; then perform approved scanning.
- Attack. Validate exploitability using the least destructive method that proves the issue. Chain weaknesses only within the approved scope and avoid unnecessary persistence or data access.
- Document impact. Record the request or command, response, affected asset, privilege gained, data reached, and any business or operational consequence.
- Report and remediate. Explain root causes, compensating controls, prioritization, and concrete fixes. A retest confirms whether the exploitable path is closed.
NIST SP 800-115 describes this practical sequence as planning, discovery, attack, and reporting or post-execution work. The phases resemble an AI red-team campaign, but the artifacts and success criteria are different.
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| Report element | AI red-team report | Penetration-test report |
|---|---|---|
| Reproduction | Prompt or content sequence, model and policy versions, context, output, and tool trace | Endpoint, request, payload or command, response, exploit chain, and affected asset |
| Boundary crossed | Safety policy, data tenant, instruction hierarchy, tool authority, or resource limit | Authentication, authorization, network segment, host privilege, or application control |
| Impact analysis | Unsafe advice, disclosure, unauthorized action, poisoned result, or resource exhaustion | Confidentiality, integrity, availability, privilege, persistence, or business impact |
| Remediation owner | Model or prompt engineering, application, data, tool-permission, trust-and-safety, or governance owner | Application, infrastructure, cloud, network, identity, platform, or security operations owner |
| Verification | Original case plus meaningful variants and regression evaluations | Retest of the exploit path and related endpoints or configurations |
Which test should you choose?
Choose AI red teaming when the uncertainty is behavioral
- The concern is prompt injection, jailbreak resistance, unsafe or misleading output, or policy inconsistency.
- The system retrieves private material, has memory, or accepts untrusted documents and web content.
- An agent can send messages, modify records, execute code, spend money, or call privileged APIs.
- You need to understand misuse scenarios, model-specific failure modes, or the effects of a model or prompt change.
Choose penetration testing when the uncertainty is technical exploitability
- The concern is an exposed service, vulnerable dependency, broken authentication, authorization bypass, or cloud misconfiguration.
- You need evidence that an attacker can move from an internet-facing endpoint to data, credentials, or administrative control.
- The product’s API, web interface, mobile application, network, identity provider, or storage layer has changed materially.
- Security operations needs validation of segmentation, logging, detection, and response against a realistic attack path.
Most AI products need both
Combine the tests when the threat model spans model behavior and the systems that enforce authority. A red team might prove that a malicious document causes an agent to request a tool call; a penetration test then verifies whether the API, token, network, and authorization layer prevent that call from changing a protected resource. Neither result alone describes the complete risk.
A practical combined test plan for an AI application
- Map the end-to-end architecture. Document clients, gateways, model endpoints, prompts, retrieval stores, queues, tools, identities, logs, and data stores.
- Separate trust boundaries. Mark user input, retrieved content, model output, tool arguments, service identities, tenant data, and administrative functions as distinct trust zones.
- Run infrastructure and API penetration tests. Validate authentication, authorization, isolation, secrets, network paths, dependencies, rate limits, and monitoring around the AI service.
- Run AI red-team scenarios. Exercise direct and indirect injection, leakage, unsafe output handling, agency, retrieval poisoning, privacy, and unbounded consumption according to the product’s threat model.
- Test the handoffs. Feed red-team outputs into the application’s browser, parser, database, template, workflow, and tool layers. Verify that each layer treats model output as untrusted.
- Test permissions with hostile context. Attempt to use injected content to cross tenants, invoke tools, access records, or bypass confirmation and approval controls.
- Prioritize chained risk. Rank combinations such as prompt injection plus excessive tool permission, retrieval leakage plus broken object authorization, or a public API plus unbounded model consumption.
- Retest after every material change. Model upgrades, new tools, altered system prompts, re-indexed data, identity changes, and new connectors can invalidate earlier conclusions.
Operational safeguards before testing
- Obtain written authorization from the system owner and define whether production is allowed.
- Use synthetic records and isolated tenants whenever they can answer the test question.
- Give agents reversible tools, least-privilege credentials, transaction caps, and explicit human approval for irreversible actions.
- Set token, time, concurrency, recursion, and spending limits before testing unbounded consumption.
- Record a stop contact and a kill switch for model endpoints, queues, tools, and network access.
- Protect transcripts and exploit evidence because they may contain secrets, personal data, or dangerous payloads.
- Coordinate with operations so alerts and outages caused by testing are recognized quickly.
Common mistakes and how to correct them
| Mistake | Why it fails | Better approach |
|---|---|---|
| Calling a vulnerability scan an AI red team | Scanners rarely test instruction hierarchy, misuse, agency, or model-specific behavior | Use scenario-driven adversarial tests with captured conversations and tool traces |
| Testing only the base model in a chat window | It misses retrieval, memory, output sinks, identity, tools, and runtime controls | Test the deployed workflow and every connected data or action path |
| Treating a system prompt as access control | Users and retrieved content can influence model behavior, while prompts do not enforce server-side authorization | Enforce permissions in APIs and services, then test them under hostile model context |
| Reporting only that a jailbreak worked | A provocative answer may have no meaningful impact, while a quiet data leak may be severe | Describe the asset, boundary crossed, repeatability, affected users, and consequence |
| Running a penetration test without rules of engagement | Real exploitation can interrupt systems or alter data | Approve scope, timing, credentials, stop conditions, and communications in writing |
| Fixing one prompt string and closing the issue | Attackers can vary wording, language, encoding, context, or delivery channel | Repair the underlying control and run regression variants |
| Ignoring cost and availability abuse | Token floods and recursive tools can exhaust budgets or deny service without a classic exploit | Test quotas, timeouts, concurrency, recursion, circuit breakers, and alerting |
Using MITRE ATLAS and other frameworks
MITRE ATLAS is a living, ATT&CK-style catalogue of AI threats grounded in observed attacks and realistic demonstrations by red teams and security groups. The page accessed in 2026 displayed 16 tactics, 208 techniques, 40 mitigations, and 73 case studies. Those counts are live-resource values and can change; they are not a fixed study statistic. ATLAS is useful for structuring scenarios and mapping mitigations, but it does not replace an architecture-specific threat model or an authorized test.
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How to interpret results
There is no authoritative single success-rate statistic that compares AI red teaming with penetration testing. A higher count of findings in one engagement does not mean that method is better; the tests explore different failure spaces.
Judge an AI red-team result by the reliability of the behavior, the boundary crossed, the affected data or action, the number of users exposed, the required privileges, and the strength of the mitigation. Judge a penetration-test result by exploitability, access gained, attack-path length, privileges, assets reached, business impact, and whether detection worked.
For both methods, a useful severity statement answers four questions: what can an attacker do, under which conditions, which control failed, and what prevents or limits the outcome. That wording gives engineering and operations teams a concrete repair target instead of an abstract model or scanner score.
Bottom line for an AI-enabled product
AI red teaming finds failures in behavior, misuse resistance, instruction boundaries, retrieval, output handling, autonomy, privacy, and resource use. Penetration testing finds exploitable weaknesses in the applications, APIs, identities, hosts, networks, cloud services, and security controls that surround the AI. If the product can both generate decisions and affect real systems, schedule both engagements under explicit authorization, connect their findings, and retest the complete workflow after fixes.
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