uro: How Pattern Matching Eliminates 90% of Reconnaissance Noise Without Making a Single HTTP Request
Hook
What if you could reduce a massive URL crawl output to actionable targets in seconds, without a single HTTP request or rate limit worry?
Context
Security researchers and penetration testers face a fundamental reconnaissance problem: modern web crawlers and archive tools generate massive URL lists where much of the content is duplicate or uninteresting. When you pull URLs from sources like Wayback Machine, a typical crawl might return /page/1, /page/2 through /page/500, or thousands of blog posts with unique paths but similar structure. Traditional deduplication with sort -u only catches exact matches, leaving you to manually scan or programmatically test large numbers of URLs that likely contain the same vulnerabilities or no vulnerabilities at all.
The naive solution—making HTTP requests to compare actual content—creates its own problems. At scale, it’s prohibitively slow, triggers rate limiting, and alerts security monitoring systems to your reconnaissance. You need a preprocessing layer that understands URL semantics without network overhead. uro solves this through pattern analysis, designed to identify structural duplicates, paginated content, human-written articles, and static assets by examining URL strings alone, without making any HTTP requests. Created by security researcher s0md3v (also known for tools like XSStrike and Photon), uro sits at the critical junction between data collection and actual security testing, turning overwhelming URL lists into more manageable attack surfaces.
Technical Insight
uro’s design is straightforward: it processes URLs without making any HTTP requests, never opening a socket or resolving DNS. This zero-network design means you can process large URL lists limited only by CPU and memory, not bandwidth or politeness delays.
The tool removes several categories of URLs: incremental URLs like /page/1/ and /page/2/, blog posts and similar human-written content like /posts/a-brief-history-of-time, URLs with the same path but different parameter values like /page.php?id=1 and /page.php?id=2, and static assets like images, JavaScript, CSS and other files considered less useful for security testing.
Here’s a practical workflow showing uro in action:
# Start with a wayback dump
waybackurls target.com > all_urls.txt
# Apply uro's default filters
cat all_urls.txt | uro > filtered_urls.txt
# Get more specific: only URLs with parameters and extensions
cat all_urls.txt | uro --filters hasparams hasext > attack_surface.txtThe filter system provides granular control over what survives the pipeline. The --filters flag accepts multiple space-separated values. Available filters include:
hasparams: only output URLs with query parameters (e.g.,http://example.com/page.php?id=)noparams: only output URLs without query parametershasext: only output URLs with file extensionsnoext: only output URLs without extensionsallexts: don’t remove any URLs based on extensionkeepcontent: keep human-written content like blogskeepslash: don’t remove trailing slashesvuln: only output URLs with parameters known to be vulnerable (references the parth project)
Whitelist and blacklist functionality operates at the extension level. By default, uro removes common static assets (images, CSS, JavaScript) that rarely contain server-side vulnerabilities. But you can override this:
# Only keep PHP and ASP pages
uro -i urls.txt -w php asp
# Blacklist specific extensions (overrides default list)
uro -i urls.txt -b jpg png js pdfNote that when using whitelist (-w), extensionless pages like /books/1 will still be included. To remove them, combine with --filter hasext. Similarly, the blacklist option overrides uro’s default list of “useless” extensions.
The keepcontent filter preserves URLs that would otherwise be removed as human-written content. By default, uro attempts to identify and remove what it determines to be blog posts or similar content, but if you’re specifically testing a CMS or looking for vulnerabilities in user-generated content, this filter keeps those URLs in your output.
The tool’s stdin/stdout design makes it composable with Unix pipelines:
# Combine multiple sources, deduplicate with uro, then probe for live endpoints
cat wayback.txt gau_output.txt crawler.txt | \
uro --filters hasparams | \
httpx -silent -mc 200 | \
tee live_targets.txtFor file-based operations, uro supports input (-i) and output (-o) flags. When writing to a file, if it already exists, uro will not overwrite the contents; otherwise, it creates a new file.
Gotcha
uro’s pattern-matching approach has inherent limitations that stem from never seeing actual content. Most significantly, it cannot distinguish between URLs that look similar but serve completely different responses. If /api/v1/users returns a user list but /api/v1/posts returns blog content, uro might treat them based on path structure patterns rather than actual functionality. You won’t know what was filtered until you’ve potentially discarded valuable targets.
The human-written content detection appears to rely on path pattern matching, though the exact implementation details aren’t documented. This could lead to false classifications: a SaaS app using descriptive workspace names might look like blog content to the filters, while a blog with minimalist URLs might slip through. The heuristics work best with conventional URL structures.
Extension-based filtering assumes file extensions accurately represent content type, which isn’t guaranteed. A misconfigured server might serve executable code with unexpected extensions, or modern frameworks might use extensionless routing for everything. The hasext filter would eliminate all those clean URLs, potentially removing interesting targets. The whitelist option helps with this by explicitly keeping only specified extensions, but you need to know what extensions your target uses.
uro processes URLs as they come and doesn’t provide an audit trail of what was removed. Once you pipe URLs through it, there’s no built-in way to see what was filtered out. If you later discover you needed certain URLs that were removed, you’ll need to regenerate your original list. The README explicitly notes that output files won’t be overwritten, suggesting you should maintain your raw crawl data separately and treat uro’s output as a derivative artifact.
The tool’s effectiveness depends heavily on your target using conventional URL patterns. Sites with unusual routing schemes, parameter structures, or naming conventions may not deduplicate as effectively as expected.
Verdict
Use uro if you’re preprocessing large-scale URL collections from sources like Wayback Machine, crawlers, or API enumeration before running active security tests. It excels when you need fast filtering to reduce reconnaissance noise, letting you focus human attention and expensive scanning tools on a more manageable set of targets. It’s particularly useful for bug bounty hunters and pentesters who regularly process massive URL dumps and need a fast first-pass filter before tools like nuclei, ffuf, or manual testing. The zero-HTTP-request design means you can process URLs quickly without triggering rate limits or alerting monitoring systems. Skip uro if you’re working with small, curated URL lists where manual review is feasible, if your target uses non-standard URL patterns that will confuse pattern-based filtering, or if you need absolute certainty that no potential target was filtered out. Also skip it if you require content-aware deduplication—at that point, you need actual HTTP requests and tools that can compare response content. Think of uro as a rough first-pass filter: it helps you focus on a more manageable subset of URLs, but you still need to verify that subset matters for your specific testing goals.
