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Use vulnerability scanning to find known or detectable weaknesses in software, hosts, and networks. Use AI red teaming to discover how an AI system behaves under adversarial prompts, misuse, unexpected inputs, and pressure on its safeguards. For most AI-enabled products, the defensible answer is both: scan the conventional technical attack surface, then red-team the model and the application context in which it operates. Neither activity proves that the other risk class is safe.
The short answer: match the test to the question
| Decision axis | Vulnerability scanning | AI red teaming |
|---|---|---|
| Primary question | What known or detectable technical weaknesses exist in the covered software, hosts, or network? | Can adversarial use or unexpected conditions make the AI fail, produce harmful output, or bypass safeguards? |
| Typical scope | Configured technical assets, services, code, dependencies, and network exposure. | Model behavior and, when included, the surrounding application, tools, data access, controls, and user interaction. |
| Best use | Repeatable discovery and tracking of technical weaknesses. | Exploration of misuse, undesirable behavior, adversarial prompts, and safeguard failure modes. |
| What it cannot establish | That an AI system behaves safely or that its safeguards withstand adversarial interaction. | That ordinary software, infrastructure, or configuration vulnerabilities have been found and fixed. |
| Recommended role in an AI deployment | Cover the conventional technical attack surface. | Probe AI-specific and deployment-context behavior. |
These are different testing objectives, not competing brands of the same test. NIST’s guidance treats technical testing as a set of methods with different benefits and limitations, while its AI guidance describes red teaming as an adversarial way to find flaws, unforeseen behavior, and misuse risks.
What vulnerability scanning does
Vulnerability scanning is an automated or semi-automated technical security test. A scanner examines the targets and checks it has been configured to assess—such as hosts, network services, web applications, software versions, dependencies, or code—against known signatures, insecure settings, exposed interfaces, and other detectable conditions.
NIST SP 800-115, Technical Guide to Information Security Testing and Assessment (September 2008), frames the larger process as planning and conducting technical tests, analyzing findings, and developing mitigation strategies. Scanning is one method in that process, alongside techniques such as penetration testing. NIST’s 2021 software-verification guidance also identifies static code scanning, fuzzing, and web-application scanners as applicable techniques; no single scanner covers every software risk.
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What a scan can reveal
- Unpatched or end-of-life components with known vulnerabilities.
- Exposed ports, services, administrative interfaces, and unsafe network paths.
- Weak configurations, missing security headers, excessive permissions, or outdated cryptographic settings when the selected scanner checks for them.
- Some coding defects, dependency problems, and web-application weaknesses through static, software-composition, dynamic, or web scanning.
- Evidence that a previously reported technical issue remains open, allowing teams to track remediation over time.
Coverage depends on the targets, credentials, network position, scan depth, signatures, and rules you select. A clean report means that the configured checks did not detect a problem; it does not mean the system has no vulnerability.
A practical scanning workflow
- Define authorization and scope. List the hosts, applications, repositories, cloud accounts, environments, and IP ranges that may be tested. Exclude third-party assets unless you have written permission.
- Choose the applicable scan types. A network scan, authenticated host scan, dependency scan, static analysis run, dynamic web scan, and fuzzing campaign answer different questions. Use the combination appropriate to the technology.
- Set safe operating conditions. Select production-safe rates, maintenance windows, credentials, and exclusions. Confirm how the tool handles denial-of-service-like payloads and sensitive data.
- Run and validate findings. Review evidence, remove false positives, and manually verify high-impact results. A scanner’s severity is an input to triage, not a complete business-risk decision.
- Remediate and retest. Patch, reconfigure, remove exposure, or accept the risk through the organization’s process. Run a targeted validation scan and record the result.
What AI red teaming does
NIST’s AI glossary defines red teaming 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.” The Generative AI Profile (July 26, 2024) presents it as an evolving practice for identifying adverse outcomes and stress-testing safeguards. Exercises can occur before or after public release; the profile’s cited discussion emphasizes pre-deployment work.
A red team does not simply submit a fixed checklist. Testers formulate hypotheses about abuse and failure, create adversarial or unexpected inputs, observe the complete system response, and adapt their probes as they learn. For an AI feature embedded in an application, the relevant system may include the model, prompt templates, retrieval layer, tools, identity and authorization controls, data stores, output filters, logging, and the user interface.
Questions a red team can investigate
- Can carefully constructed prompts circumvent refusal, policy, or content safeguards?
- Does the model disclose secrets from prompts, retrieved documents, conversation history, logs, or connected tools?
- Can untrusted content influence tool calls, instructions, routing, or permissions?
- Does the system produce unsafe, discriminatory, misleading, or otherwise undesirable output under unusual context, language, or formatting?
- What happens when inputs are extremely long, contradictory, malformed, multimodal, or deliberately ambiguous?
- Do rate limits, monitoring, escalation, and human-review controls continue to work during sustained misuse?
- Can an attacker turn a model error into an application, privacy, financial, or operational impact?
A practical AI red-team workflow
- Describe the system and threat model. Record model versions, system prompts, tools, data sources, user roles, trust boundaries, allowed actions, and the harms you are trying to prevent.
- Set rules of engagement. Specify environments, test accounts, prohibited actions, sensitive data handling, stop conditions, escalation contacts, and whether testing is pre-release, staged, or production.
- Define failure criteria. Examples include unauthorized tool execution, disclosure of protected data, bypass of a required refusal, materially unsafe advice, or failure to route a high-risk case to a human.
- Build diverse test cases. Combine direct misuse prompts with indirect instructions in retrieved content, multilingual and obfuscated requests, role-play, context switching, long-context pressure, malformed inputs, and realistic user workflows. The exact set should follow the system’s risks rather than a generic prompt list.
- Probe adaptively. Vary wording, sequence, identity, permissions, context, and tool state. Preserve the conversation and system state for each reproducible finding.
- Assess impact in context. Determine what the observed behavior permits in the deployed application. A surprising answer in an isolated model is different from an unauthorized transaction or data disclosure through a connected tool.
- Remediate and rerun. Adjust prompts, policies, model configuration, retrieval, permissions, filters, monitoring, or human controls. Retest the original case and nearby variants to detect regressions.
NIST cautions that red-team findings need interpretation before they are used in governance or risk decisions. Output quality depends partly on the testers’ background and expertise, so a small exercise with narrow skills can miss important failure modes.
Why a vulnerability scan cannot test an AI model’s safety
A scanner may identify the server hosting a model, an exposed API, a vulnerable library, or an insecure web endpoint. Those are valuable findings. It normally does not determine whether a model follows an instruction hierarchy, resists prompt injection, refuses dangerous requests consistently, leaks information through conversation context, or uses an authorized tool safely. Those behaviors depend on inputs, state, policies, model capabilities, and application logic rather than only on a known software signature.
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Conversely, a red team may demonstrate a harmful response or a prompt-injection path without discovering that the application also contains an unpatched framework, exposed database, or weak authentication control. Treating a red-team report as a substitute for technical discovery leaves ordinary attack paths unexamined.
When to choose one first
Start with vulnerability scanning when
- The immediate concern is an internet-facing host, API, web application, cloud configuration, dependency, or endpoint with an unknown patch state.
- You need a repeatable inventory of technical weaknesses before a release or compliance review.
- The AI component is not yet connected to sensitive data or privileged actions, but its surrounding infrastructure is being deployed.
- You are triaging a newly acquired environment and do not yet know what assets are exposed.
Start with AI red teaming when
- The model can generate decisions, content, code, recommendations, or actions whose failure could harm users or the organization.
- The main uncertainty concerns jailbreaks, prompt injection, data leakage, unsafe output, bias, hallucination-related harm, or control bypass.
- The application gives the model access to tools, private retrieval data, transactions, files, or administrative workflows.
- A model, prompt, retrieval source, policy, or tool configuration has changed and its behavioral safeguards must be re-evaluated.
Run both in parallel when
An AI-enabled system has a conventional attack surface and meaningful model-driven risk—which is the normal case for a production service. Scanning can proceed on hosts, code, dependencies, and interfaces while a red team tests the model and its surrounding controls in an isolated environment. Coordinate windows and accounts so one activity does not contaminate the evidence of the other.
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| Phase | Technical testing | AI-focused testing | Decision output |
|---|---|---|---|
| 1. Scope | Inventory hosts, services, code, dependencies, interfaces, and environments. | Map models, prompts, data, tools, roles, safeguards, and possible harms. | Approved targets, owners, access, and stop conditions. |
| 2. Baseline | Run authenticated and unauthenticated scans appropriate to each asset. | Establish expected behavior with benign and policy-compliant cases. | Prioritized technical and behavioral baselines. |
| 3. Adversarial exercise | Validate important scanner findings and perform authorized technical tests. | Use adaptive prompts, indirect instructions, unusual inputs, and abuse workflows. | Reproducible evidence and impact assessments. |
| 4. Fixes | Patch, reconfigure, remove exposure, or formally accept a risk. | Change model, prompts, retrieval, permissions, filters, monitoring, or human review. | Named remediation owners and deadlines. |
| 5. Verification | Run a targeted rescan and confirm closure. | Replay failed cases, test variants, and check for regressions. | Release or continued-risk decision supported by evidence. |
Keep the findings in one risk process but preserve their different evidence. A CVE-style technical issue, a prompt-injection reproduction, and an unsafe recommendation may require different owners and controls even when they affect the same product.
How to plan a credible AI red team
Define the deployment context
Record where the model runs, who can use it, what information it can retrieve, which tools it can call, and what each tool is permitted to do. The same model behavior can have very different consequences in a public chatbot, an internal assistant, and an agent authorized to modify records.
Use varied expertise
Include people who understand machine learning, application security, privacy, abuse cases, the relevant business process, and the populations affected by the system. NIST specifically notes that tester background and expertise influence the quality of red-team outputs. External specialists can help when internal staff lack experience with adversarial machine-learning techniques, but the system owner must still provide context and make risk decisions.
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Make evidence reproducible
- Capture model and application versions, configuration, system prompts, retrieval documents, tool permissions, timestamps, and account role.
- Save the exact input sequence and resulting output, including safety-filter or monitoring events.
- Describe the expected behavior, observed failure, realistic impact, exploit prerequisites, and confidence.
- Separate a model tendency from an application control failure and from a business-process failure.
Interpreting results and setting priorities
Do not rank findings by scanner severity or by how dramatic a model output appears in isolation. Assess exploitability, required access, affected users, data sensitivity, reversibility, scale, detectability, and the strength of existing controls. A low-probability prompt trick that can trigger a privileged transaction may outrank a frequent but harmless odd response. A critical infrastructure or regulated deployment may also require stricter thresholds than an experimental internal tool.
For scanning, confirm the asset and vulnerable version, determine exposure, apply the vendor or internal fix, and verify that the finding closes. For red teaming, identify whether the remedy belongs in the model, instruction hierarchy, retrieval pipeline, authorization layer, output handling, monitoring, rate limiting, or human approval. A prompt-only change is not a sufficient fix when the model still has excessive privileges.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Common mistakes and how to correct them
Using a scanner as an AI safety test
Problem: The team reports “no vulnerabilities” and treats the model as safe.
Correction: Scan the infrastructure and run a separately scoped behavioral exercise with explicit misuse and safeguard criteria.
Calling a scripted prompt checklist a red team
Problem: Testers run a small static set of examples and miss adaptive attack paths.
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Correction: Let testers vary context, sequence, language, permissions, retrieved content, and tool state; preserve failed and successful variants.
Testing only the base model
Problem: The exercise ignores application prompts, retrieval, tools, identity, and output handling.
Correction: Test the deployed system and clearly label any isolated-model results as limited evidence.
Skipping authorization and safety controls
Problem: An exercise causes an outage, exposes real personal data, or crosses a legal boundary.
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Correction: Use written rules of engagement, synthetic or minimized data, test accounts, stop conditions, and an escalation path.
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Failing to retest after a fix
Problem: The original example passes while a nearby variant still succeeds, or a fix breaks a legitimate workflow.
Correction: Replay the finding, test related variants, and run regression cases for both safety and normal functionality.
What current NIST guidance says—and what it does not
NIST SP 800-115 remains foundational general information-security testing guidance, but its September 2008 publication makes it non-AI-specific. Pair its process advice with current AI guidance rather than presenting it as an AI testing standard.
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The NIST AI Risk Management Framework is intended for voluntary use. NIST says the framework is being revised and notes that a profile concept note for trustworthy AI in critical infrastructure was released on April 7, 2026. Do not treat an unfinished revision or a voluntary framework as a universal legal requirement.
NIST’s ARIA Evaluation Planning Manual: Elements of ARIA-Style AI Evaluations (September 18, 2026) describes a broader evaluation model: “The ARIA approach assesses an AI system’s trustworthiness by combining data from three types of testing: Model Testing, Red Teaming, and User Testing.” Red teaming is therefore one part of a complete evaluation program, not the entire program.
How to interpret the ARIA pilot numbers
NIST’s November 2025 Assessing Risks and Impacts of AI (ARIA): Pilot Evaluation Report reports 51 red teamers participating in ARIA 0.1 between December 2024 and January 2025, and 19 field testers participating in January 2025. Those are counts for that pilot only. They are not staffing recommendations, minimum team sizes, or comparative evidence that one method is more effective than the other.
A release checklist
- Technical assets, code, dependencies, interfaces, and cloud resources have an owner and an approved scan scope.
- Relevant scan types have run with appropriate credentials, exclusions, and production safeguards.
- Important technical findings have been validated, assigned, remediated, and retested.
- The AI system’s model, prompts, retrieval, tools, roles, data, and safeguards are documented.
- Red-team rules of engagement, test accounts, data handling, stop conditions, and success criteria are approved.
- Adversarial and unexpected behaviors have been tested in the deployed context, not only against an isolated model.
- Findings have reproducible evidence, impact analysis, owners, deadlines, and regression tests.
- Residual risk is accepted by the authorized decision-maker, with monitoring and reassessment triggers.
Bottom line
Choose vulnerability scanning for detectable technical weaknesses and AI red teaming for adversarial behavior, misuse, and safeguard resilience. An AI product usually needs both, supplemented where appropriate by model testing and user testing. Define scope, access, conditions, and success criteria before either activity, interpret results in the actual deployment context, and feed remediation into the same organizational risk process.
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