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Introduction:
In an era where phishing campaigns are becoming increasingly sophisticated, the line between a legitimate notification and a malicious trap is often blurred by clever social engineering. The following analysis dissects a real-world investigation into a suspicious email masquerading as a GitHub notification. By leveraging a combination of Generative AI for initial triage, URL analysis platforms, and interactive malware sandboxes, the analyst uncovered a fraudulent scheme designed to redirect victims to a cryptocurrency wallet-draining website. This article provides a technical walkthrough of the investigation process, highlighting the essential tools and methodologies for modern threat intelligence.
Learning Objectives:
- Understand how to use Large Language Models (LLMs) for initial phishing email triage and context analysis.
- Learn to utilize URLScan.io to map redirect chains and identify malicious infrastructure.
- Master the use of ANY.RUN for interactive sandbox analysis to observe live JavaScript execution and wallet connection traps.
1. Initial Triage with Generative AI (LLMs)
The investigation began with a standard security practice: treating every unexpected notification with skepticism. Before clicking any link, the analyst copied the raw email content into a Generative AI tool (like GPT).
Step‑by‑step guide explaining what this does and how to use it:
1. Extract Raw Email: Copy the entire email body, including headers if possible, but at minimum the text and links (do not click them).
2. Prompt Engineering: Ask the AI for a general opinion and risk assessment. A useful prompt structure is:
“Analyze the following email for indicators of phishing or social engineering. Identify any urgent language, mismatched sender details, or suspicious URLs. Email content: [Paste Email]”
3. Analyze Output: The AI will flag inconsistencies, such as language that doesn’t match official GitHub communications or the presence of URL shorteners/redirects. This provides a rapid, unbiased first impression and helps prioritize which emails warrant deeper investigation.
2. Mapping the Redirect Chain with URLScan.io
With the email flagged as suspicious, the next step was to understand the destination of the link without directly exposing a corporate asset to danger. URLScan.io is a free service that takes a URL and captures a screenshot and the resulting DOM and redirect paths.
Step‑by‑step guide explaining what this does and how to use it:
1. Submit the URL: Go to `urlscan.io` and submit the suspicious link extracted from the email.
2. Review the Redirects: Once the scan completes, navigate to the HTTP Transactions tab. This section lists every request made by the browser, including redirects (HTTP 301/302). Look for the final destination URL.
3. Analyze the Destination: In this case, the scan revealed a redirect to a site related to “TRX” (Tron cryptocurrency) featuring a fake office address in London. This immediately confirmed the email was not a GitHub notification but a crypto-focused lure.
– Linux/CLI Alternative: You can use `curl -L` to follow redirects from the terminal.
curl -L -I "http://suspicious-link.com"
3. Geolocation and Infrastructure Recon
After identifying the fake “London office” address in the URLScan report, the next step was to verify its legitimacy and correlate it with the digital infrastructure. This blends OSINT with technical analysis.
Step‑by‑step guide explaining what this does and how to use it:
1. Address Validation: Use Google Maps or a business registry to check if the company exists at the provided address. Often, scammers use real addresses of unrelated businesses to lend false credibility.
2. WHOIS Lookup: Perform a WHOIS lookup on the final domain. Look for recent creation dates, redacted or suspicious registrant info, and nameservers associated with bulletproof hosting.
– Command: `whois malicious-trx-site.com`
3. IP Geolocation: Check the IP address of the server. Does it actually reside in London, or is it hosted in a country with lax cybercrime laws? A mismatch between the claimed physical address and server location is a major red flag.
4. Dynamic Analysis in ANY.RUN Sandbox
Static analysis suggested a threat, but dynamic analysis was required to confirm the payload. ANY.RUN is an interactive sandbox that allows you to “click” on the live site in a secure, isolated environment.
Step‑by‑step guide explaining what this does and how to use it:
1. Start the Task: In ANY.RUN, create a new task and input the final malicious URL. Select a Windows environment for the analysis.
2. Interact with the Page: Once the desktop loads, the browser will open the page. Observe the content. In this case, the page mimicked a platform called “OpenClaw,” but instead of a service, it offered “free money.”
3. Stimulate the Attack: Click the “Connect Wallet” button. In a real sandbox, this triggers the malicious JavaScript. ANY.RUN will capture the resulting network traffic, processes spawned, and any file drops.
– Windows Monitoring: Open Process Explorer (included in the sandbox) to see if `powershell.exe` or `wscript.exe` is spawned by the browser, which is a common sign of a drive-by download.
5. JavaScript Analysis and Indicator Extraction
The final piece of the puzzle was the client-side code. Within the ANY.RUN environment, the analyst inspected the page source and discovered a JavaScript file named eleven.js. This file contained the logic for the wallet drainer.
Step‑by‑step guide explaining what this does and how to use it:
1. Locate the Source: In the ANY.RUN browser, right-click and select “View Page Source” or use the browser’s Developer Tools (F12) to navigate to the Sources tab.
2. Identify Malicious Scripts: Look for obfuscated or uniquely named JavaScript files (e.g., eleven.js, `web3.min.js` but loaded from a strange path).
3. Deobfuscate and Extract IOCs: Copy the script content. If it’s obfuscated, use tools like `de4js` or manual analysis to decode it. Look for:
– Hardcoded Wallet Addresses: The attacker’s cryptocurrency wallet address where funds are sent.
– API Endpoints: Calls to Web3 providers or custom backend APIs to facilitate the theft.
– Linux Command: Use grep, awk, and `sort` to extract unique domains or addresses from the JS file.
cat eleven.js | grep -Eo '(http|https)://[^/"]+' | sort -u
cat eleven.js | grep -Eo '0x[a-fA-F0-9]{40}' | sort -u
6. Mitigation: Blocking the Infrastructure
Once the Indicators of Compromise (IOCs)—domains, IPs, and wallet addresses—are extracted, the final step is operationalizing this threat intelligence for defense.
Step‑by‑step guide explaining what this does and how to use it:
1. DNS Blocking: Add the malicious domain to your organization’s DNS sinkhole (e.g., Pi-hole, Cisco Umbrella) to prevent any user from resolving it.
2. Firewall Rules: Create outbound firewall rules to block traffic to the specific IP addresses hosting the phishing kit.
3. Endpoint Protection: Deploy YARA rules or EDR signatures to detect the `eleven.js` file or similar scripts attempting to execute on endpoints.
– Example YARA Rule Snippet:
rule detect_eleven_drainer {
strings:
$js_identifier = "eleven.js" nocase
$web3_connect = "connectWallet" nocase
condition:
all of them
}
What Undercode Say:
- The Human Element is Still the Primary Vector: Despite the technical sophistication of the wallet drainer, the attack started with a simple email. Technical controls failed because the user was the first line of defense.
- Layered Analysis is Non-Negotiable: Relying on a single tool (like an email filter) is insufficient. This investigation shows the power of a layered approach: AI for triage, URLScan for recon, and ANY.RUN for deep dive analysis. Each tool answered a question the previous one could not.
This incident highlights the convergence of traditional phishing techniques with the unique attack surface of Web3. The attackers weren’t stealing passwords; they were manipulating the blockchain connection process itself. By training users and analysts to follow this investigative playbook, organizations can effectively neutralize threats that exist at the intersection of email, the web, and cryptocurrency.
Prediction:
As browser-based cryptocurrency wallets become more mainstream, we will see a sharp rise in “drainer-as-a-service” kits. These attacks will evolve beyond simple JavaScript to leverage WebAssembly (WASM) for obfuscation and performance, making them harder to detect in sandboxes that rely solely on static JavaScript analysis. The future of this threat landscape will involve AI-generated phishing lures that dynamically adapt to the victim’s on-chain activity to appear even more legitimate.
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Reported By: Itai Raz1 – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
