When approaching bug bounty programs or security assessments, one of the most overlooked areas is JavaScript files. These files often hide sensitive information, forgotten endpoints, or misconfigured tokens.
Reading JavaScript “like a hacker” means not just opening them in a browser, but systematically extracting, analyzing, and scanning them for secrets.
In this guide, we’ll walk through a workflow that uses command‑line tools to uncover hidden treasures inside JS files. Each command is explained in detail, and filenames are slightly altered to keep the workflow unique.
Step 1: Collect JavaScript Endpoints
cat urls_list.txt | subjs | sort -u > collected_js.txt
What it does:
- cat urls_list.txt → reads a file containing HTTP endpoints.
- subjs → extracts JavaScript file references from those endpoints.
- sort -u → sorts them uniquely, removing duplicates.
- Output → collected_js.txt now contains a clean list of JS files.
This is your starting point: a curated list of JavaScript files linked to the target application.
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Step 2: Deep Crawl for Hidden JS
cat urls_list.txt | subjs | sort -u | katana -jc | grep -Ei “\.js(\?|$)” > deep_scan_js.txt
What it does:
- katana -jc → crawls deeper into the site, following JavaScript references.
- grep -Ei “\.js(\?|$)” → filters only .js files, even if they have query strings.
- Output → deep_scan_js.txt contains JS files discovered beyond the surface.
This step ensures you don’t miss deeply nested scripts that aren’t obvious in the initial crawl.
Step 3: Secret Hunting in Hidden Files
while read link; do SecretFinder -i “$link” -o cli; done < hidden_js.txt > secrets_output.txt
What it does:
- Loops through each URL in hidden_js.txt.
- Runs SecretFinder to detect API keys, tokens, or credentials.
- Outputs results into secrets_output.txt.
This is where the real hacking mindset kicks in — scanning for sensitive strings that developers may have left behind.
Step 4: Grep for Common Secrets
grep -r -E ‘aws_access_key|aws_secret_key|api_key|passwd|pwd|firebase|swagger|password|.env|ssh_key|oauth_token’ collected_js.txt
What it does:
- Searches recursively for keywords that often indicate secrets.
- Targets AWS keys, Firebase configs, .env references, and OAuth tokens.
This is a quick win technique: many times, developers accidentally hardcode credentials in JS files.
Step 5: Automated Scanning with Nuclei
nuclei -l collected_js.txt -tags js,secrets,exposure,token -o js_scan_results.txt
What it does:
- Uses nuclei templates to scan JavaScript files for exposures.
- Tags focus on secrets, tokens, and misconfigurations.
- Results saved in js_scan_results.txt.
This step automates vulnerability detection, combining your manual hunting with structured scans.
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Step 6: Alternative Quick Extraction
cat urls_list.txt | subjs | grep -Ei “\.js(\?|$)” | sort -u > quick_js.txt
What it does:
- A faster variation of Step 1.
- Directly extracts JS files without deep crawling.
- Useful when you want a lightweight scan.
Step 7: Expanding with Wayback Machine URLs
waybackurls targetsite.com | grep “\.js” | sort -u > archived_js.txt
What it does:
- waybackurls pulls historical URLs from the Wayback Machine.
- grep “\.js” filters only JavaScript files.
- sort -u ensures uniqueness.
- Output → archived_js.txt contains JS files that may no longer be live but still reveal sensitive code.
This is powerful because old JS files often contain forgotten API keys or endpoints that developers thought were gone.
Step 8: Using gau for Mass Enumeration
gau targetsite.com | grep “\.js” | sort -u > gau_js.txt
What it does:
- gau (GetAllURLs) collects URLs from multiple sources (Wayback, Common Crawl, etc.).
- Filters .js files.
- Output → gau_js.txt gives you a broad set of JavaScript references.
This complements waybackurls by pulling from different archives and feeds.
Step 9: Validating Endpoints with httpx
cat gau_js.txt | httpx -mc 200 -silent > live_js.txt
What it does:
- httpx checks which JS URLs are still live.
- -mc 200 ensures only HTTP 200 OK responses are kept.
- Output → live_js.txt is a verified list of accessible JS files.
This step saves time by removing dead links before deeper analysis.
Step 10: Extracting Endpoints from JS
grep -Eo ‘(https?://[^”]+)’ live_js.txt | sort -u > endpoints.txt
What it does:
- Uses regex to extract URLs inside JS files.
- Collects API endpoints, hidden paths, or third‑party integrations.
- Output → endpoints.txt becomes a roadmap of potential attack surfaces.
This is where you start pivoting from JS analysis to API testing.
Step 11: Searching for Sensitive Configurations
grep -i -E ‘config|token|auth|secret|key|firebase’ live_js.txt > config_hits.txt
What it does:
- Targets keywords that often indicate sensitive configurations.
- Output → config_hits.txt highlights suspicious lines worth manual review.
This is a quick filter to catch misconfigured integrations.
Step 12: Combining with Nuclei Templates
nuclei -l endpoints.txt -tags exposure,api,token -o endpoint_scan.txt
What it does:
- Runs Nuclei against discovered endpoints.
- Focuses on exposure and token leaks.
- Output → endpoint_scan.txt provides structured vulnerability findings.
This bridges the gap between manual discovery and automated testing.
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Step 13: Bonus — Extracting Comments from JS
grep -E “//|/\*” live_js.txt > comments.txt
What it does:
- Extracts developer comments inside JS files.
- Comments often reveal internal notes, TODOs, or deprecated endpoints.
Sometimes the most valuable intel comes from what developers casually leave behind.
Conclusion and Best Practices
By now, you’ve seen how a hacker‑style workflow for reading JavaScript files can uncover hidden secrets, forgotten endpoints, and misconfigured tokens. The commands we walked through from subjs and katana to gau, httpx, and nuclei form a layered approach that combines manual discovery with automated scanning.
But tools alone don’t make you effective. Here are some best practices to keep in mind:
1. Diversify Your Sources
- Don’t rely only on live JS files.
- Use archived sources (waybackurls, gau) to find forgotten scripts.
- Validate with httpx to avoid wasting time on dead links.
2. Always Verify Findings
- A regex hit doesn’t mean a real secret.
- Cross‑check suspicious strings with context before reporting.
- Example: An api_key string might be a placeholder, not a live credential.
3. Respect Scope and Compliance
- Only test within authorized scope.
- Avoid scanning unrelated domains or third‑party scripts unless explicitly allowed.
- This keeps your work professional and audit‑ready.
4. Document Everything
- Save outputs (collected_js.txt, deep_scan_js.txt, endpoint_scan.txt).
- Screenshots and logs help clients understand your findings.
- Documentation also supports compliance frameworks like ISO 27001.
5. Think Like a Developer
- Look at comments (grep -E “//|/\*”) for hints.
- Developers often leave TODOs or notes that point to hidden functionality.
- This mindset helps you anticipate where secrets might be buried.
6. Automate, But Don’t Blindly Trust Automation
- Tools like nuclei are powerful, but they can miss context.
- Manual review of JS files often reveals subtle issues automation skips.
- Balance speed with depth.
Final Thoughts
Reading JavaScript like a hacker isn’t about memorizing commands, it’s about building a workflow that uncovers what others overlook. By chaining together tools (subjs, katana, gau, httpx, SecretFinder, nuclei), you create a pipeline that transforms raw URLs into actionable intelligence.
If you’re doing any of bug bounty hunting, penetration testing, or compliance audits, this approach ensures you don’t miss the hidden gems inside JS files. And remember: every JS file is a potential map to the backend, treat it with the same curiosity and rigor as you would any exposed API.
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