Traditional blocklists are static text files that enumerate known-bad domains. They were built for an era when humans clicked links and security teams had days to update rules. Autonomous browser agents move faster, encounter more domains, and require blocking logic that understands categories, page types, and risk context — not just domain strings.
A manually curated blocklist of 50,000 domains covers less than 0.05% of the active internet. Your agent encounters the rest unchecked.
Most domain blocklists used in enterprise security are community-maintained lists of known malicious, adult, or phishing domains. Lists like the Steven Black hosts file or the EasyList filter set contain between 30,000 and 200,000 entries. They are updated periodically by volunteers who submit and review entries. For human browsing filtered through a DNS resolver, these lists provide reasonable coverage of the most egregious domains.
For autonomous browser agents, these lists fail in four fundamental ways:
Replace your static domain list with a dynamic, category-driven blocking system. Our 102M domain database classifies every domain with IAB v3 taxonomy categories, web filtering labels, page-type identifiers, reputation scores, and popularity rankings. Instead of maintaining a list of specific blocked domains, you define blocking rules at the category level: block all domains classified as "Adult," block all pages typed as "login," block all domains with reputation scores below a threshold.
This approach scales automatically. When a new adult site is registered today, it gets classified when it appears in the database or via the real-time API fallback — and your category-level block rule catches it without any manual list update. Your blocklist effectively becomes a policy engine that operates on structured metadata rather than raw domain strings. One rule — "block web filtering category: Adult" — replaces tens of thousands of individual domain entries.
Three layers of blocking intelligence that replace static lists with dynamic, context-aware rules
Define blocking rules at the IAB taxonomy level. Instead of listing individual adult domains, block the entire "Adult" web filtering category — a single rule that covers hundreds of thousands of domains. Add category-level blocks for Malware, Phishing, Gambling, Weapons, and any other classification that violates your agent's operating policy. The database resolves every URL to its categories, and the rule evaluates in microseconds.
Block specific page types regardless of domain category. Login pages, checkout flows, admin panels, and settings pages all represent interaction surfaces where agents should not operate. A single rule — "block page type: login" — prevents your agent from reaching login forms across every domain in the database, without needing to enumerate each domain individually.
Block domains below a reputation threshold. The database includes OpenPageRank scores and global popularity rankings for every domain. Set a rule that blocks any domain with a PageRank below 2 or outside the top 10 million — filtering out newly registered, parked, or low-quality domains that are statistically more likely to host malicious content or misleading information.
Production-ready snippets for building category-aware blocklists for browser agents
import http.client
import json
class CategoryAwareBlocklist:
"""Dynamic blocklist that uses domain categorization
instead of static domain lists."""
BLOCKED_WEB_FILTER_CATS = [
"Adult", "Malware", "Phishing", "Gambling",
"Weapons", "Illegal Content", "Drugs"
]
BLOCKED_PAGE_TYPES = [
"login", "signup", "checkout", "admin", "settings"
]
MIN_REPUTATION_SCORE = 2 # Block low-rep domains
def __init__(self, api_key):
self.api_key = api_key
self.conn = http.client.HTTPSConnection(
"www.websitecategorizationapi.com"
)
self.local_cache = {}
def is_blocked(self, target_url):
"""Check if a domain should be blocked based on
category, page type, or reputation rules."""
if target_url in self.local_cache:
return self.local_cache[target_url]
payload = (
f"query={target_url}"
f"&api_key={self.api_key}"
f"&data_type=url"
f"&expanded_categories=1"
)
headers = {
"Content-Type": "application/x-www-form-urlencoded"
}
self.conn.request(
"POST",
"/api/iab/iab_web_content_filtering.php",
payload,
headers
)
res = self.conn.getresponse()
data = json.loads(res.read().decode("utf-8"))
# Check web filtering categories
filter_cat = (
data.get("filtering_taxonomy", [[""]])[0][0]
.replace("Category name: ", "")
)
if filter_cat in self.BLOCKED_WEB_FILTER_CATS:
result = (True, f"Blocked category: {filter_cat}")
self.local_cache[target_url] = result
return result
# Check page type
page_type = data.get("page_type", "unknown")
if page_type in self.BLOCKED_PAGE_TYPES:
result = (True, f"Blocked page type: {page_type}")
self.local_cache[target_url] = result
return result
result = (False, "Domain allowed")
self.local_cache[target_url] = result
return result
# Usage in browser agent
blocklist = CategoryAwareBlocklist(api_key="your_api_key")
blocked, reason = blocklist.is_blocked("https://example.com")
if blocked:
print(f"Navigation denied: {reason}")
else:
print("Navigation permitted — proceeding")
class AgentBlocklistEngine {
constructor(apiKey, blockRules) {
this.apiKey = apiKey;
this.blockRules = blockRules;
this.cache = new Map();
}
async checkDomain(targetURL) {
if (this.cache.has(targetURL)) {
return this.cache.get(targetURL);
}
const response = await fetch(
"https://www.websitecategorizationapi.com" +
"/api/iab/iab_web_content_filtering.php",
{
method: "POST",
headers: {
"Content-Type": "application/x-www-form-urlencoded"
},
body: new URLSearchParams({
query: targetURL,
api_key: this.apiKey,
data_type: "url",
expanded_categories: "1"
})
}
);
const data = await response.json();
const filterCat =
data.filtering_taxonomy?.[0]?.[0]
?.replace("Category name: ", "") || "Unknown";
const pageType = data.page_type || "unknown";
let decision = { blocked: false, reason: "Allowed" };
if (this.blockRules.categories.includes(filterCat)) {
decision = {
blocked: true,
reason: `Category "${filterCat}" is blocked`
};
} else if (this.blockRules.pageTypes.includes(pageType)) {
decision = {
blocked: true,
reason: `Page type "${pageType}" is blocked`
};
}
this.cache.set(targetURL, decision);
return decision;
}
}
// Usage
const engine = new AgentBlocklistEngine("your_api_key", {
categories: ["Adult", "Malware", "Gambling", "Phishing"],
pageTypes: ["login", "checkout", "admin", "settings"]
});
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Also available: Enterprise URL Database up to 102M domains from $2,499. View all database tiers →
Search any IAB or Web Filtering category to see how many domains your blocklist rules would cover in our 102M Enterprise Database.
How 102 million domains from our main Enterprise Database are distributed across IAB v3 taxonomy classifications
Spanning Tier 1 through Tier 4 classifications from our 102M Enterprise Database
Charts display domain counts for the top 50 out of 700+ categories in our 102M Enterprise Database. To check the number of domains for the remaining 650+ categories, use the Category Counter tool above .
The domain blocklist has been a staple of internet security since the mid-1990s. The concept is simple: maintain a list of domains known to host malicious, inappropriate, or unwanted content, and block access to those domains at the DNS, proxy, or firewall level. For three decades, this approach worked well enough for human users because human browsing is predictable, limited in volume, and already filtered through layers of judgment and institutional knowledge.
Autonomous browser agents shatter every assumption that made static blocklists workable. An AI agent does not have institutional knowledge about which domains are risky. It does not exercise judgment about whether a URL looks suspicious. It follows links, executes searches, and navigates wherever its instructions or discovered URLs point it — at a rate of hundreds or thousands of page visits per hour. A static blocklist covering 200,000 domains is a speed bump on a highway with 350 million exits.
Consider the coverage arithmetic. The internet has approximately 350 million registered domain names. The most comprehensive public blocklist — the combined Steven Black hosts list — contains roughly 180,000 entries. That covers 0.05% of all registered domains. Even if you aggregate every public blocklist available — DNS-based, browser extension, and enterprise — you reach perhaps 2 million unique domain entries, or 0.57% coverage. Your agent encounters the other 99.43% of the internet with zero blocking guidance.
Our 102M domain database inverts this arithmetic. Instead of listing 200,000 bad domains, you have 102 million classified domains. Instead of checking "is this domain on the bad list," you check "what category is this domain" and apply category-level rules. A single rule blocking the "Adult" web filtering category blocks every adult domain in the database — not 200,000 of them, but millions. A single rule blocking "login" page types blocks login pages across all 102 million domains.
The fundamental conceptual shift is from deny-listing to policy-driven access control. A deny-list says "these specific domains are blocked, everything else is allowed." A policy-driven system says "domains in these categories are blocked, pages of these types are blocked, domains below this reputation are blocked — and everything else can be evaluated case by case." The first approach requires you to enumerate every threat. The second approach requires you to define your policy, and the database handles the enumeration.
This shift is particularly powerful when you consider emerging threats. A new phishing domain registered today will not appear on any static blocklist for days or weeks. But if the domain is classified via the real-time API as "Phishing" in the web filtering taxonomy, your category-level block rule catches it immediately. The blocklist updates itself because the classification system continuously evaluates new domains.
An effective blocklist for autonomous agents operates at multiple tiers. The first tier is web filtering categories — hard blocks on categories that represent clear risks: Adult, Malware, Phishing, Illegal Content, Gambling, Weapons, and Drugs. These are non-negotiable blocks that apply to every agent regardless of its task.
The second tier is page-type blocks — universal restrictions on page types that agents should never interact with: login, signup, checkout, admin, and settings pages. These blocks prevent agents from reaching authentication surfaces, payment flows, and administrative interfaces even on otherwise allowed domains.
The third tier is reputation-based filtering — blocking domains with low OpenPageRank scores or no global popularity ranking. Newly registered domains, parked pages, and low-quality sites are disproportionately likely to host phishing, malware, or misleading content. A reputation threshold acts as a catch-all for domains that are not explicitly categorized as threats but share the risk profile of threat domains.
The fourth tier is task-specific allowlisting — for agents with narrow task scopes, define an allowlist of IAB categories relevant to the task and block everything else. A financial research agent gets access to "Business and Finance" and "News" categories; a product research agent gets access to "Shopping" and "Technology & Computing." Everything outside the allowlist is blocked by default.
Static blocklists stored in memory as hash sets provide O(1) lookup time. Our 102M domain database, loaded into Redis, matches this performance — sub-millisecond lookups for any domain. The database is larger (approximately 15GB in raw form, compressed to 4GB), but modern servers handle this easily. A single Redis instance can serve thousands of lookups per second, more than enough for even the most aggressive agent deployment.
For organizations that cannot deploy the full database locally, the real-time API provides classification on demand with average latency under 200ms. The recommended architecture uses the local database for the 99.5% of domains that are pre-classified and falls back to the API for the 0.5% of unknown or newly registered domains. This hybrid approach delivers both the performance of a local blocklist and the coverage of a real-time classification service.
Unlike static blocklists that require daily updates from community maintainers, a category-aware blocklist separates the rules from the data. Your blocking rules — which categories, page types, and reputation thresholds to block — change infrequently, perhaps quarterly as your security team refines the policy. The underlying domain data updates quarterly through database refreshes, which add newly classified domains and update categories for domains that have changed content.
This separation of concerns simplifies maintenance dramatically. Your security team manages a policy document with perhaps 20-30 rules. The database team manages the quarterly data refresh. Neither depends on the other for day-to-day operation. Compare this to a static blocklist where every new domain entry requires someone to discover the domain, verify it is malicious, add it to the list, and push the update to all consuming systems.
A category-aware blocklist does not replace your existing security infrastructure — it extends it to cover agent traffic. The blocking decisions made by the database-backed system should feed into your SIEM for correlation with other security events. If an agent is repeatedly hitting blocked categories, that pattern might indicate prompt injection or task drift. If multiple agents across your organization are encountering the same unknown domain, that domain deserves investigation by your threat intelligence team.
The structured nature of the blocking data — categories, page types, reputation scores — makes it ideal for SIEM correlation rules. Set up alerts for agents that exceed blocking thresholds. Create dashboards that show blocking rates by agent, by category, and by time period. Use the data to continuously refine your blocking policies based on actual agent browsing patterns rather than theoretical threat models.
Stop maintaining lists of individual domains. Deploy a category-aware blocking system backed by 102 million classified domains that scales automatically with the internet.