AI crawler policy is becoming part of ordinary website operations. A site owner may want pages discoverable in AI search while preferring that the same content is not used for model training. Another site may want to block automated crawlers from a private documentation area while leaving public marketing pages available. Those are different decisions, and treating every AI-related user agent as one thing is where policies become confusing.
The practical starting point is not to copy a giant robots.txt block from social media. It is to decide what outcome you actually want, identify which crawler controls correspond to that outcome, publish a narrow policy, and then verify what is visible from the public internet. Vulnify's AI Access Checker is useful for that verification step because it compares crawler rules with the live homepage and also inspects signals such as llms.txt and same-origin agent paths. It does not grant or enforce crawler access on your behalf.
Start with three different AI access questions
Before editing robots.txt, separate three questions that are often collapsed into one.
- Should this content appear in AI-assisted search? Search-oriented crawlers may need permission to fetch public pages so those pages can be surfaced in answers or search experiences.
- Should this content be available for model training? Some providers publish a separate crawler or control token for training-related collection.
- Should a user-triggered assistant be able to fetch a page? A user asking an assistant to open a specific URL is not always the same thing as an automated crawler indexing the web.
OpenAI documents this separation explicitly. OAI-SearchBot is used for ChatGPT search, while GPTBot is the crawler associated with content that may be used to improve generative AI foundation models. ChatGPT-User is used for some user-triggered actions and is not an automatic web crawler. That distinction matters because a single blanket rule can accidentally block a capability you wanted to keep.
Google uses a different model for Google-Extended. It is a robots.txt product token that controls whether content Google crawls may be used for certain Gemini training and grounding uses. Google states that Google-Extended does not affect normal Google Search inclusion or ranking. In other words, the same robots.txt file can contain controls for very different products.
Build the policy from intent, not from a copied list
A useful policy starts with content classification. Public product pages, documentation intended for broad discovery, customer-only areas, staging paths, administrative routes, and legal documents do not all deserve the same rule.
For example, an organization may decide that public documentation can appear in AI search but should not be used for training. A simplified OpenAI policy could look like this:
User-agent: OAI-SearchBot
Allow: /
User-agent: GPTBot
Disallow: /
User-agent: *
Allow: /That example expresses one narrow decision. It does not say that every AI system on the internet will respect the rule, and it does not make private content private. robots.txt is a crawler instruction mechanism, not an authorization layer. Sensitive material still requires authentication, access control, and appropriate network exposure.
If the website has a public knowledge base but a private support area, scope the paths deliberately instead of blocking the entire domain:
User-agent: GPTBot
Allow: /docs/
Disallow: /account/
Disallow: /support/private/
Disallow: /admin/The paths above are illustrative. Do not publish sensitive route names merely because they appear in an example. If a route is meant to be private, protect it with real authorization. A disallow rule can actually advertise the existence of a path to anyone who reads robots.txt.
Google-Extended is not Googlebot
This is one of the easiest policy mistakes to make. Blocking Googlebot affects Google Search crawling. Blocking Google-Extended is a separate decision. If the business depends on organic search, do not assume that a generic "block AI" snippet leaves search unaffected. Check the provider's current documentation before deploying a policy.
A narrow Google example might be:
User-agent: Google-Extended
Disallow: /
User-agent: Googlebot
Allow: /The value of writing rules this way is clarity. Six months later, a developer can read the file and understand that the organization intended to preserve Google Search crawling while opting out of the Google-Extended use case. That is much safer than a long list of user agents copied without context.
Anthropic also separates training, search, and user-directed access
Anthropic currently documents three separate robots. ClaudeBot is used to collect public web content that may contribute to model training. Claude-SearchBot is used to improve search results for Claude users, and Claude-User is used when a person asks Claude to retrieve web content. Anthropic says these bots honor robots.txt controls.
That means the same policy question appears again: do you want to restrict model-development crawling while keeping search-oriented or user-directed retrieval available? If so, express those choices separately rather than blocking every Claude-related user agent with one rule.
User-agent: ClaudeBot
Disallow: /
User-agent: Claude-SearchBot
Allow: /
User-agent: Claude-User
Allow: /As with the OpenAI examples, verify Anthropic's current documentation before deployment because crawler names and product behavior can change. Apply the policy separately to each subdomain you operate rather than assuming one robots.txt file governs an entire domain namespace.
Treat llms.txt as a signal, not an access-control system
You may also encounter llms.txt. It is best treated cautiously. It can be useful as a machine-readable way to point assistants toward preferred documentation or describe important resources, but it should not be treated as a replacement for robots.txt, authentication, or provider-specific crawler controls. Support and interpretation can vary.
Vulnify's AI Access Checker reads llms.txt as one of several public signals. That is useful because the operational question is not only "does the file exist?" but "does the collection of public instructions make sense together?" A site can have an llms.txt file that points to documentation while robots.txt blocks the crawler expected to retrieve it. That is a policy mismatch worth noticing.
Verify the live site, not just source control
Many crawler-policy failures are deployment failures rather than syntax failures. A repository contains the intended robots.txt, but the CDN serves an older copy. A framework route generates a different file in production. A proxy rewrites the response. A staging environment gets promoted with a global disallow rule. These are ordinary operational mistakes.
After publishing a change, fetch the actual public file at /robots.txt and confirm the response you expect. Then check the homepage and any paths whose access policy matters. If your stack generates robots.txt dynamically, test more than one deployment region or hostname when relevant.
This is where Vulnify fits naturally. Use the AI Access Checker to review the public crawler posture, then compare the output with the policy decision you documented internally. The checker can show what a public observer sees. It cannot tell you whether the business decision itself is correct.
Common AI crawler policy failures
Blocking everything by accident
A broad User-agent: * disallow can affect far more than AI training. If organic search, monitoring, link previews, or other automated clients matter to the site, review the blast radius before deploying a wildcard rule.
Assuming Disallow protects secrets
It does not. robots.txt is public. If a page contains customer data, internal documentation, source maps, backup files, or administrative functionality, use access controls. Vulnify's Exposed Paths Checker can help review publicly reachable sensitive-path indicators, but remediation belongs in the application, web server, storage policy, or deployment process.
Mixing search and training controls
Provider controls are not interchangeable. OpenAI separates search and training crawlers. Google-Extended is explicitly separate from Google Search. Other providers publish their own rules and may change them over time. Keep a short internal record of why each rule exists, then review the provider documentation periodically.
Forgetting subdomains
A policy on www.example.com does not automatically tell you what is being served from docs.example.com, help.example.com, or app.example.com. Treat each public hostname as its own surface. Vulnify's Passive Subdomain Discovery can help produce candidates that deserve review.
A practical review workflow
Use a simple sequence rather than making crawler policy a one-time legal or SEO exercise.
- List the public hostnames and content areas that matter.
- Decide separately whether each area should support search discovery, training collection, and user-triggered retrieval.
- Read the current provider documentation for the crawlers you intend to control.
- Write the smallest robots.txt rules that express those decisions.
- Keep private content behind authentication regardless of crawler rules.
- Deploy the policy and verify the public response.
- Check for contradictions between robots.txt, llms.txt, redirects, and the live page.
- Recheck after CDN, framework, hosting, or domain changes.
This workflow also creates a useful audit trail. If someone later asks why GPTBot is blocked while OAI-SearchBot is allowed, the answer is not "because somebody copied a snippet." It is tied to an explicit content-use decision.
Use Vulnify as an external verification layer
Vulnify is most useful here as a public-surface validation layer. The AI Access Checker can compare crawler rules with the live site and inspect related public signals. The broader tools catalog can then help with adjacent checks such as exposed paths, robots and sitemap behavior, subdomains, redirects, and security headers.
That boundary is important. Vulnify does not negotiate rights with AI providers, publish your robots.txt file, or replace access control. The product helps you see what is exposed and whether the public configuration matches what you thought you deployed.
Final check
AI crawler access should be an intentional policy, not a copied blocklist. Separate search, training, and user-triggered retrieval. Protect private content with real authorization. Keep provider-specific controls narrow. Then verify the deployed result from outside your own infrastructure.
The useful question is not "are AI bots allowed?" It is "which automated uses do we intend to allow for which content, and does the live site actually express that decision?" Once the question is framed that way, robots.txt becomes much easier to manage.