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World desk4 min

The Good Bot/Bad Bot Binary Is Dead: How Sites Should Judge Crawlers

“Good bot” and “bad bot” labels are too blunt for site policy. Judge each crawler by verified identity, purpose, return, resource cost, and the controls available to your site.

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“Good bot” and “bad bot” are not reliable site policies. A crawler’s identity alone does not reveal what it does, whether it sends readers back, or what it costs to serve. Site owners should verify who is connecting, assess the crawler’s purpose and site-specific value, and then monitor, limit, or block access according to their own priorities. Credential stuffing and vulnerability probing remain clear hostile activity; the point is not that every bot is ambiguous, but that broad labels are a poor substitute for evidence.

Why the good-bot/bad-bot split fails

A crawler can identify itself honestly without being useful to every site. Conversely, a familiar company name or a “verified” label does not establish that a particular request benefits a publisher. As Krystal O’Connor, senior product manager at WP Engine, puts it: “A verified bot represents itself truthfully, but verification is a statement about honesty, not business value.”

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Purpose can also be difficult to infer from identity. WP Engine notes that Google’s AI Overviews and AI Mode are part of Google Search; a publisher may not be able to distinguish that activity from traditional search indexing based on the crawler identity alone. The practical policy question is not simply whether an operator is reputable, but what the crawler is doing with the site’s content and what the site receives in return.

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That makes “good” and “bad” contextual judgments. A crawler that brings valuable discovery traffic to one publisher may offer little return to another; a crawler’s resource use or content use may also matter differently depending on the site’s priorities.

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What to assess before allowing or blocking a crawler

IAB Tech Lab’s CoMP guidance recommends evaluating individual crawlers rather than assigning one judgment to an entire company, since an operator may run separate crawlers for different functions. The following dimensions help make the decision explicit; they are not a formula that automatically produces the right answer.

  • Identity confidence: Can you verify the operator beyond the user-agent string?
  • Purpose and content use: Is the crawler performing search discovery, AI training, inference, monitoring, security, scraping, or another task? Can you distinguish those functions in practice?
  • Traffic return: Does the crawler send readers to the site or provide another measurable benefit?
  • Infrastructure cost: How much bandwidth, origin capacity, or service capacity does it consume?
  • Reputation and strategic fit: Does the activity fit the site’s business model and content strategy?
  • Control and reversibility: Can you monitor it, apply a narrower rule or rate limit, and revisit the decision when evidence changes?

These questions help answer concerns such as, “How much money am I spending delivering my content to bots and crawlers?” and whether a site can allow an operator’s search crawler for discovery while blocking that operator’s use of content for AI training. If a tool cannot distinguish those functions, the site may have to choose a broader rule or accept less control.

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Verify identity; do not rely on a label

User-agent strings are declarations, not proof: they can be spoofed. Cloudflare advises pairing allowlists with other signals, such as behavioral analysis or machine learning, rather than trusting a user-agent allowlist by itself. WP Engine also identifies published IP lists, stable user agents, reverse DNS, and cryptographic request signatures as possible verification signals.

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Verification answers who appears to be making a request; it does not settle whether the request is desirable. A dashboard that reports “verified bots” is useful only if the site understands how verification works and separately considers the crawler’s purpose, return, and cost. Cloudflare describes robots.txt as a starting point, but notes that some bots may disregard it, so it should not be mistaken for an enforcement mechanism.

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Build visibility before changing access rules

IAB Tech Lab cautions against making broad access decisions without understanding the traffic already reaching a site. Blocking without that visibility can cut off a crawler that drives referrals; allowing without it can leave the site serving resource-intensive activity with no meaningful return. Monitoring provides a way to identify what is happening before making an irreversible-seeming choice—though access decisions can be reversed.

  1. Establish what is reaching the site. Review crawler traffic and available logs or bot-management reporting so you can identify patterns and estimate resource use.
  2. Check identity signals. Do not treat a bot’s name or user-agent as conclusive; use verification signals available through your infrastructure.
  3. Assess purpose and return. Compare the crawler’s apparent content use and referral or other value with the bandwidth and capacity it consumes.
  4. Choose the narrowest workable control. If the concern applies to a specific function or behavior, prefer a targeted rule or rate limit over a blanket block when your tools and evidence allow it.
  5. Monitor the result and reassess. Check whether the rule changes referrals, resource use, or unwanted activity, and adjust it as your priorities or evidence change.

Some distinctions may not be technically available: if one operator’s crawler identity covers multiple functions, a site may not be able to permit one use while refusing another. Make the policy with that limitation in mind rather than assuming a label or control can separate activities it cannot identify.

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Bot categories are not universal judgments

Bot classifications depend on context and on who is doing the classifying. NetScaler’s April 2026 signature update includes good and bad classifications among AI crawlers, search engines, and scrapers. Those are labels in that product’s signature set, not universal verdicts on every crawler in any of those categories.

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Thales’s 2026 Bad Bot Report, analyzing bot activity across full-year 2025, says bots made up 53% of internet traffic, human traffic 47%, and bad bots 40%. Thales also reports a 12.5-fold year-over-year increase in AI-enabled bot attacks and says it blocked 17.2 trillion bot requests in 2025. These figures describe Thales’s own report and methodology; they are not an independently harmonized census of all internet traffic. The scale figures underline why bot activity matters, but they do not tell an individual site which crawler to allow.

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