Mining CommonCrawl for Forgotten URLs: A Deep Dive into cc.py
Hook
CommonCrawl archives billions of web pages going back to 2008. Most security researchers never tap into this archive of historical URLs, freely available for passive reconnaissance.
Context
Traditional web reconnaissance forces a choice: actively scan your target and risk detection, or rely on limited real-time data from search engines. Bug bounty hunters and penetration testers constantly need to map attack surfaces—finding subdomains, API endpoints, admin panels, and forgotten infrastructure—but aggressive scanning triggers WAFs, gets IP addresses blocked, and alerts security teams.
CommonCrawl changes this equation. This non-profit has performed monthly crawls of billions of web pages, storing the results in publicly accessible archives. The data sits there, waiting to be queried. But interfacing with CommonCrawl isn’t trivial: you need to understand their index structure and handle large result sets. cc.py emerged as a focused Python wrapper that does one thing well—extract every URL CommonCrawl has ever seen for a target domain, with performance optimizations to make it practical for real-world reconnaissance workflows.
Technical Insight
At its core, cc.py is a multithreaded client that translates simple domain queries into CommonCrawl requests. The tool fetches available crawl snapshots (monthly indexes like CC-MAIN-2018-05 as shown in the README examples), then constructs queries to match your target domain.
The architecture uses temporary file writing to handle result sets that can easily exceed available memory. When you run python3 cc.py github.com -y 2018 -o github_18.txt, cc.py appears to use multithreading (the README notes ‘Implementation of multithreading’ and claims ‘65% faster proceeding’ in v0.3) to query different indexes concurrently, writing results to temporary files before consolidating them into your specified output. This design trades disk I/O for memory stability—crucial when a single query might return hundreds of thousands of URLs.
Here’s a practical reconnaissance workflow using cc.py:
# Extract all URLs from 2018
python3 cc.py github.com -y 2018 -o github_18.txt
# Filter for interesting endpoints
cat github_18.txt | grep user
cat github_18.txt | grep -E '\.(js|json|xml)$' > github_assets.txt
cat github_18.txt | grep -i admin > potential_admin.txtThe year filtering (-y/--year) is particularly valuable for targeted reconnaissance. If you know a target underwent a major infrastructure migration in 2019, querying pre-migration data reveals legacy systems that might still be accessible. The --list flag shows all available indexes, letting you understand temporal coverage before committing to a full crawl.
For comprehensive historical analysis, the --index flag crawls all pages within a specific CommonCrawl snapshot: cc.py army.mil -i CC-MAIN-2018-05. This crawls all pages for that index as noted in the README, though it significantly increases runtime. The multithreading implementation makes this more feasible.
The tool’s simplicity is intentional. It doesn’t attempt deduplication, URL parsing, or intelligent filtering—that’s left to standard Unix tools as shown in the README’s grep example. This design philosophy keeps cc.py maintainable and composable. You pipe results through grep, sort, uniq, or other tools for URL analysis:
python3 cc.py target.com -y 2020 -o results.txt
cat results.txt | sort -u > unique_urls.txtOne architectural detail worth noting: cc.py writes to temporary files during processing (marked as implemented in the TODO list), then consolidates output. This prevents memory exhaustion but means you’ll need adequate disk space when querying popular domains across multiple years.
Gotcha
The fundamental limitation is data freshness—CommonCrawl indexes are historical snapshots, not real-time data. If you’re hunting for newly deployed microservices or recently launched subdomains, cc.py will miss them entirely, making this strictly a historical reconnaissance tool.
More frustrating is the lack of built-in deduplication. A popular domain across multiple years of crawls will generate massive output files filled with duplicate URLs. You’ll spend significant time post-processing with sort and uniq. The README acknowledges this by showing grep as the expected filtering mechanism, but there’s no native support for advanced filtering at query time.
The repository itself raises maintenance concerns. The README explicitly states ‘This is a fork from the main repository, i just added some missing features’ but doesn’t link to the upstream project or explain the fork rationale. The TODO list shows ‘direct-grep’ remains unimplemented—a feature that would presumably enable better filtering. For a tool with 275 stars, you’re adopting something that works but may not evolve if CommonCrawl changes their API.
Verdict
Use cc.py if you’re conducting passive reconnaissance on established web properties where historical data matters—discovering forgotten subdomains that still resolve, identifying deprecated API endpoints that lack modern authentication, or mapping infrastructure changes over time without sending a single packet to the target. It excels in bug bounty initial reconnaissance phases and red team engagements where stealth matters more than completeness. Skip it if you need real-time enumeration, require sophisticated filtering beyond grep, or want actively maintained tooling with clear upstream support. For those scenarios, you’ll need to evaluate alternative tools or build directly against CommonCrawl’s infrastructure. cc.py occupies a narrow niche: quick historical URL dumps for security researchers comfortable with Unix pipelines and willing to trade feature richness for simplicity.
