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Introduction:
In the cybersecurity landscape, the human element remains the weakest link. A seemingly innocuous social media post, like the one teased by Julien Metayer with a simple “Vous la voyez venir ?” (Do you see it coming?), serves as a perfect trap for Open Source Intelligence (OSINT) gathering. This article explores how threat actors and security professionals alike use OSINT to extract critical technical data from casual online interactions, turning a simple “like” or image into a gateway for deeper reconnaissance and potential exploitation.
Learning Objectives:
- Understand the core principles of OSINT and how social media platforms become data goldmines.
- Learn to extract geolocation data and metadata from images using command-line tools.
- Identify the technical indicators (links, headers, server info) hidden within social media posts.
You Should Know:
1. Image Forensics and EXIF Data Extraction
When a user posts an image, they often unknowingly share a wealth of information embedded in the file. Exchangeable Image File Format (EXIF) data can include GPS coordinates, camera model, timestamp, and even the software used to edit the photo. In a penetration testing scenario, this is the first step in building a physical profile of a target.
Step‑by‑step guide (Linux/macOS):
To extract this data, we use exiftool, a powerful command-line utility.
1. Installation (if not present):
sudo apt-get install exiftool Debian/Ubuntu or brew install exiftool macOS
2. Extraction: Assume the image is downloaded as post_image.jpg.
exiftool post_image.jpg
3. Analysis: Look specifically for the `GPS Position` tag. If present, you can input these coordinates directly into mapping software.
Example output filter exiftool post_image.jpg | grep -i gps
Windows Equivalent: Right-click the image file > Properties > Details. However, for bulk analysis or scripting, tools like `ExifTool` are also available for Windows via command line.
2. Reverse Image Search and Geolocation
If EXIF data is stripped (as many platforms do), the visual context remains. Attackers use reverse image search to find other instances of the same image or similar landscapes to pinpoint location.
Step‑by‑step guide (Browser/Linux CLI):
- Manual Browser Method: Save the image. Go to Google Images or TinEye and upload the file.
- Automated CLI Method (using `curl` and APIs): While full reverse image search requires an API key (Google Custom Search JSON API), you can fetch the image headers to verify its origin.
Check where the image is hosted and its metadata via HTTP headers curl -I "https://media.licdn.com/dms/image/v2/example.jpg"
This reveals the server type (e.g.,
x‑li‑tracking) and CDN, which can be useful for infrastructure mapping.
3. URL Extraction and Link Analysis
The post mentions “Visit my website” or contains graphic links. Every URL in a post is a potential attack vector. OSINT analysts extract these to perform domain profiling.
Step‑by‑step guide:
- Extract URLs from text: If you have the raw text of the post, you can use `grep` to find links.
echo "Check this: https://example.com and http://test.net" | grep -oE 'https?://[^ ]+'
- Analyze the Domain: Once you have the URL, use `whois` and `nslookup` to gather infrastructure data.
whois example.com | grep -E 'Registrar|Creation Date|Name Server' nslookup example.com
This reveals the hosting provider and potentially the registrant’s information if not privacy-protected.
4. The “Like” and “Reaction” Recon
The post shows reactions from users like “Jackson ADJIBI” and “Benjamin Touchard.” In a social engineering engagement, these are primary targets. They have publicly associated themselves with the content creator.
Step‑by‑step guide (OSINT Framework):
- Username Correlation: Take the names visible in the reactions (e.g., “Maxence R.”). Use tools like `whatsmyname.app` (CLI version) or `sherlock` to see if that username is used on other platforms.
Install Sherlock (Python tool) git clone https://github.com/sherlock-project/sherlock.git cd sherlock python3 sherlock --help Usage (if username is maxencer) python3 sherlock maxencer
This reveals if the user has a GitHub, Twitter, or other accounts, broadening the attack surface.
5. Social Media API Manipulation
Professional OSINT practitioners rarely use the browser interface; they interact directly with the platform’s API to bypass rate limits and scrape data.
Step‑by‑step guide (Linux – `curl`):
While LinkedIn heavily restricts unauthenticated access, you can test the endpoints.
1. Fetch Public Profile Data:
Hypothetical endpoint (structure changes frequently) curl -A "Mozilla/5.0" "https://www.linkedin.com/in/tonymoukbel/" | grep -oE 'experience|education|skills'
Note: This often returns a login wall. In these cases, analysts move to Google dorks (e.g., site:linkedin.com "Tony Moukbel") to see what Google has cached.
6. Header Analysis and Tracking Pixels
The phrase “View
’s graphic link" implies that the post contains images that are potentially loaded with tracking pixels. These pixels tell the poster exactly who viewed the content and from what IP address. <h2 style="color: yellow;">Step‑by‑step guide (Inspection):</h2> <ol> <li>Inspect Element: Right-click on the image in a browser > Inspect.</li> <li>Look for Tracking: Look for `<img src="..."` tags that contain parameters like `?utm_source=` or <code>?trk=</code>. These are unique to the viewer and serve as a reverse-OSINT trap (the viewer reveals themselves to the poster).</li> <li>Blocking Tracking: To prevent this, users can use browser extensions like uBlock Origin or Privacy Badger, or utilize the `hosts` file to block known tracking domains. [bash] Windows Hosts File (C:\Windows\System32\drivers\etc\hosts) Linux/macOS Hosts File (/etc/hosts) Add line: 0.0.0.0 tracking.linkedinsights.com
What Undecode Say:
- Key Takeaway 1: The “Bait” is a two-way street. While the poster uses the image to engage followers, the followers’ reactions (likes, comments) expose their digital identities to the poster. In offensive security, the audience is just as valuable a target as the original speaker.
- Key Takeaway 2: Metadata is the silent witness. Even if a user refrains from commenting, simply loading the image in their browser can send HTTP headers and expose their IP address to the CDN hosting the image, creating a digital trace.
Analysis:
The exchange between Yannick Le Guédart (“It’s a trap !”) and Guy Arbus (“La carotte, on la voit venir de loin”) highlights the cybersecurity community’s acute awareness of social engineering. However, awareness alone is insufficient without technical controls. The “trap” referenced is not just the visual punchline of the image, but the invisible data collection mechanisms embedded in the post. Professionals must treat every public interaction as a potential data leak. The fusion of human curiosity (clicking the image) with technical naivety (not blocking trackers) creates the perfect conditions for a successful OSINT operation, turning a casual scroll into a detailed behavioral profile.
Prediction:
As AI-driven OSINT tools become more accessible, the latency between a post going live and a comprehensive digital dossier being compiled will shrink from hours to milliseconds. We will see a rise in “anti-OSINT” tools that inject fake data into tracking pixels and spoof user agents, leading to an arms race where social media platforms become battlefields of data deception rather than simple communication tools. The “bait” will evolve into AI-generated deepfakes designed specifically to lure and profile specific high-value individuals.
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IT/Security Reporter URL:
Reported By: Jmetayer Osint – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
