Use vulnerability scanning to discover known or detectable weaknesses in hosts, software and networks. Use AI red teaming to challenge an AI system with adversarial prompts, misuse attempts and unexpected conditions, then examine whether its safeguards fail. For most AI-enabled products, the sound choice is both: scan the conventional technical attack surface and red-team the model, application behavior and user interactions that create AI-specific risk.
The two activities answer different security questions. Treating one as a substitute for the other leaves a material part of the system unexamined.
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What each approach is designed to find
AI red teaming
NIST defines red teaming in the AI context as “a structured testing effort, often adopting adversarial methods, to find flaws and vulnerabilities in an AI system, including unforeseen or undesirable system behaviors or potential risks associated with the misuse of the system.” In practice, a team deliberately tries to make the system behave in an unsafe, unreliable or policy-breaking way.
Testers may use adversarial prompts, prompt-injection attempts, unusual input combinations, role-play, data-extraction requests, abuse scenarios and stress conditions. The objective is not simply to produce a failure. It is to understand the conditions that trigger it, the harm it could cause, whether a safeguard detects or blocks it, and how the surrounding product responds.
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NIST’s Generative AI Profile describes red teaming as an evolving practice for identifying adverse behavior or outcomes and stress-testing safeguards. It can take place before or after public release; the profile’s cited discussion focuses on pre-deployment work. Findings require interpretation before they are used in governance or risk decisions. NIST also notes that output quality depends partly on the testers’ background and expertise.
Vulnerability scanning
Vulnerability scanning is a technical security-testing method that checks configured hosts, software, services or networks for known or detectable weaknesses. Depending on the scanner and its configuration, it may identify outdated components, exposed services, insecure settings, missing patches, weak configurations or recognizable software flaws.
NIST SP 800-115 places scanning within a broader process: plan the test, conduct technical examinations, analyze findings and develop mitigation strategies. Scanning is listed alongside other methods, including penetration testing, because every method has different benefits and limitations. NIST’s software-verification guidance also recommends techniques such as static code scanning, fuzzing and web-application scanners where they apply. A scan is therefore one component of a verification program, not a complete security assessment.
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| Decision axis | Vulnerability scanning | AI red teaming |
|---|---|---|
| Main question | What known or detectable technical weaknesses exist in the covered software, hosts or network? | Can adversarial use or unexpected conditions make the AI system fail, produce harmful output or bypass safeguards? |
| Typical scope | Technical assets and software; coverage depends on configured targets and scanning method. | Model behavior and, when included in scope, the surrounding application, safeguards and user interaction. |
| Useful when | You need a repeatable way to identify and track technical weaknesses. | You need to explore misuse, adversarial prompts, undesirable behavior or safeguard failure modes. |
| Primary output | Technical findings that can be triaged, assigned and remediated. | Reproducible behavior examples, attack paths, impact analysis and recommendations for model or product controls. |
| What it cannot establish alone | That AI behavior is safe or that safeguards withstand adversarial interaction. | That ordinary software, infrastructure and configuration vulnerabilities have been discovered. |
| Best role in an AI product | Examine the conventional technical attack surface. | Probe AI-specific and deployment-context behavior. |
The final two rows are practical implications of the methods’ differing scopes. Neither method, by itself, demonstrates that the whole system is secure.
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When vulnerability scanning should come first
Start with scanning when your immediate question concerns the technical foundation rather than the model’s judgment or conversation behavior. This is usually the case when:
- An internet-facing host, API, container, dependency or web application is being deployed or materially changed.
- You need an inventory of detectable weaknesses and a repeatable way to track remediation over time.
- A patch, configuration change or newly disclosed software issue could alter exposure.
- Your organization has a vulnerability-management process with defined owners, severity rules and remediation deadlines.
- The AI feature is only one component of a conventional application whose infrastructure has not yet received routine testing.
Define the assets, network locations, credentials, scan mode, exclusions and test window before running the scan. A finding is not automatically a confirmed exploitable vulnerability; verify its accuracy, assess the affected asset’s role and prioritize it in context. NIST SP 800-115 emphasizes analysis and mitigation planning rather than treating a scanner’s output as the final result.
What scanning can reveal in an AI deployment
- An exposed management interface or service used by the AI application.
- Outdated libraries in an inference server, web front end or data-processing pipeline.
- Insecure transport, authentication or authorization settings around an AI API.
- Web-application weaknesses that could let an attacker reach prompts, files or administrative functions.
- Misconfigured storage, logs or network controls that expose input data or model artifacts.
These are important attack paths, but a clean scan does not show whether a model can be manipulated into disclosing secrets, generating dangerous instructions or bypassing a policy.
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When AI red teaming should come first
Prioritize red teaming when the principal uncertainty is how the AI system behaves under pressure. It is especially valuable when:
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- The product accepts natural-language or multimodal input from untrusted users.
- The model can call tools, retrieve documents, write code, send messages or take other consequential actions.
- Safety filters, refusal policies, classifiers or human-review gates are central to the product’s risk controls.
- The system handles sensitive data and could be induced to reveal information through prompts or context manipulation.
- Normal quality testing looks acceptable, but misuse, edge cases or adversarial behavior have not been explored.
- A major model, prompt, retrieval, tool or policy change is approaching release.
Scope the exercise around realistic abuse cases, not only a list of prohibited phrases. Include the model and the system around it where relevant. A model embedded in an application may have privileges, data access and controls that materially change the risk.
Questions a red team can answer
- Can an attacker override instructions or cause the model to ignore a safety policy?
- Can retrieved content or a tool response inject instructions that alter the model’s behavior?
- Does the system reveal system prompts, credentials, personal data or confidential business content?
- Can a sequence of individually harmless requests produce a harmful result?
- Do refusal and moderation controls work consistently across languages, formats and paraphrases?
- Can the model trigger an external action without the required authorization or human confirmation?
- Are monitoring, rate limits and incident controls activated when abuse is attempted?
Record the exact inputs, conversation state, model and application versions, enabled tools, retrieved context, output, control response and potential impact. Reproducibility lets engineers determine whether the problem belongs in the model, prompt, retrieval layer, tool permission, interface or operational process.
What a scanner will miss—and what a red team will miss
Blind spots of vulnerability scanning
A scanner generally evaluates technical conditions that its signatures, probes or analysis rules can recognize. It does not infer whether an otherwise valid response is dangerous, whether a refusal can be bypassed through a multi-turn exchange or whether a tool-using model is making an unsafe decision. It also cannot establish that a safeguard is resilient to adversarial interaction simply because the surrounding endpoint has no known software weakness.
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Blind spots of AI red teaming
Red teaming can expose a serious behavioral failure while missing an unpatched server, an exposed database, a weak access-control rule or a vulnerable dependency. Its results also depend on test design, tester expertise, available time, model access and the realism of the deployment context. A narrow prompt-only exercise may say little about risks introduced by the application’s permissions, data flows or infrastructure.
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For these reasons, a “pass” in either activity should be stated narrowly: pass the defined scan scope or pass the tested behavioral scenarios. Neither is a universal security certification.
How to choose the right test or combine both
Use this decision sequence before scheduling work:
- State the risk question. Write whether you are looking for technical weaknesses, unsafe AI behavior, safeguard bypasses, misuse paths or a combination.
- Map the system. Identify hosts, code, APIs, models, prompts, retrieval stores, tools, identities, data and human-review points. Note which components are production, staging or isolated test assets.
- Set access and conditions. Define authenticated and unauthenticated views, model version, enabled tools, data sensitivity, rate limits, test accounts, logging and the permitted test window.
- Choose the method that matches the question. Use scanning for detectable technical weaknesses and red teaming for adversarial or unexpected behavior. Add other verification methods where appropriate, such as static analysis, fuzzing, penetration testing or user testing.
- Define success criteria. For a scan, specify coverage, severity thresholds, validation and remediation ownership. For red teaming, specify harmful outcomes, safeguard expectations, reproducibility and escalation rules.
- Analyze findings in deployment context. Consider exploitability, affected privileges, data sensitivity, likely users, existing controls and whether a failure is isolated or systemic.
- Remediate and retest. Patch or reconfigure technical weaknesses; revise prompts, policies, filters, permissions, retrieval controls or workflows for behavioral findings. Re-run the relevant test after changes.
- Feed results into risk governance. Track accepted risks, owners, deadlines, evidence and residual risk rather than filing findings away as disconnected test reports.
A workable combined program for an AI-enabled product
Before development or release
Inventory the architecture and perform baseline technical testing on the environments and services that will host the product. At the same time, create an abuse-case catalog for the model and its surrounding application. Include unauthorized data access, prompt injection, harmful content, unsafe tool use, policy evasion and denial-of-service or resource-exhaustion scenarios where relevant.
During integration testing
Scan changed hosts, dependencies, APIs and web components. Red-team the integrated system rather than only an isolated model: test retrieval, memory, tools, authentication, user roles, moderation and human approval. This is where a model behavior that looked acceptable in a laboratory can become risky because the application grants it new privileges or data.
At release gates
Set separate release criteria for technical and behavioral findings. A critical infrastructure weakness may block release even when red-team results are clean; a reproducible safeguard bypass may block release even when the scanner reports no high-severity issue. Document any accepted exceptions and their compensating controls.
After release
Schedule recurring scans according to your vulnerability-management process and repeat targeted red-team exercises after model, prompt, retrieval, tool, policy or user-interface changes. Monitor production signals for new abuse patterns, then turn credible incidents and near misses into new test cases.
How current NIST guidance fits the decision
| Guidance | Date | How it informs the choice |
|---|---|---|
| ARIA Evaluation Planning Manual: Elements of ARIA-Style AI Evaluations | September 18, 2026 | Frames trustworthy-AI assessment as a combination of model testing, red teaming and user testing. |
| NIST AI glossary, red-teaming entry sourced to AI 100-2e2025 | 2025 source | Defines AI red teaming as structured, often adversarial testing for flaws, undesirable behavior and misuse risk. |
| SP 800-115: Technical Guide to Information Security Testing and Assessment | September 2008 | Provides foundational planning, testing, analysis and mitigation guidance for technical security assessments; it is not AI-specific. |
| Guidelines on Minimum Standards for Developer Verification of Software | October 6, 2021 | Places static code scanning, fuzzing and web-application scanners among applicable verification techniques. |
| AI Risk Management Framework: Generative AI Profile | July 26, 2024 | Characterizes red teaming as an evolving way to identify adverse outcomes and stress-test safeguards. |
| Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations | March 24, 2025 | Provides current terminology and categories for adversarial attacks, lifecycle stages, attacker goals, capabilities and mitigations. |
| ARIA: Pilot Evaluation Report | November 2025 | Reports on an evaluation pilot that included 51 red teamers and 19 field testers; these are pilot participation counts, not staffing benchmarks. |
NIST says its AI Risk Management Framework 1.0 is voluntary and is being revised. An AI RMF profile concept note for trustworthy AI in critical infrastructure was released on April 7, 2026. Do not treat the framework as a binding universal requirement or describe a 2026 revision as finalized.
Interpreting results and avoiding common mistakes
Do not compare raw finding counts
One scan may produce hundreds of technical findings while a red-team exercise produces a handful of high-impact behavioral failures. The counts are not comparable measures of effectiveness. No comparative efficacy statistic establishes that one approach is better than the other.
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Validate scanner results and determine the affected asset’s actual exposure. For red-team findings, assess the likelihood of the behavior, the users or data it affects, the controls that failed and the consequences if an attacker repeats it.
Do not run a generic exercise against a changing target
Record versions and conditions. A model update, system-prompt change, new retrieval source or newly granted tool permission can invalidate an earlier result. Retest the changed path instead of assuming old evidence remains current.
Do not use pilot participation as a staffing rule
The ARIA 0.1 pilot involved 51 red teamers between December 2024 and January 2025 and 19 field testers in January 2025. Those figures describe that pilot only; they do not indicate how many people an organization needs or predict testing effectiveness.
Bottom line for security leaders and developers
Choose vulnerability scanning when the decision concerns known or detectable weaknesses in your technical assets. Choose AI red teaming when the decision concerns adversarial behavior, misuse, harmful output or safeguards. If the product is AI-enabled, plan both tracks, connect their findings through the same risk process and add model testing and user testing where the system’s trustworthiness depends on them. The strongest assessment is a scoped program in which each method answers the question it is capable of answering.
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