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Introduction:
The rapid adoption of Generative AI (GenAI) has introduced a new, high-stakes battleground within enterprise cybersecurity. Threat actors are now leveraging large language models (LLMs) to rapidly generate malicious code, embedding remote access Trojans (RATs) and information stealers into browser extensions disguised as AI productivity tools. These “High-risk AI extensions” exploit their privileged positions within the browser to intercept sensitive data flowing through AI services like ChatGPT, effectively turning the browser into a primary attack surface and demanding immediate, robust defensive actions.
Learning Objectives:
- Learn to identify and detect malicious browser extensions that utilize AI-generated code to exfiltrate data.
- Master command-line techniques for Windows, Linux, and macOS to audit browser extensions for potential threats.
- Understand and implement enterprise browser hardening and extension governance policies to mitigate these emerging risks.
You Should Know:
- Detecting Malicious Browser Extensions via Command Line (Windows, Linux, macOS)
The first line of defense is proactive detection. You must regularly audit installed browser extensions for suspicious activity, starting with command-line introspection.
Many malicious extensions, like those discovered by Unit 42, operate by injecting scripts that override network request APIs before calls leave the page. A key Indicator of Compromise (IoC) is an extension communicating with a recently registered or low-reputation domain.
Here’s how to audit your systems across all major platforms:
Windows (PowerShell & PowerShell Core): Locate and list all Chrome/Edge extensions.
List all Chrome extensions
Get-ChildItem -Path "$env:LOCALAPPDATA\Google\Chrome\User Data\Default\Extensions\" -Directory | ForEach-Object {
$manifest = Get-Content "$($<em>.FullName)\manifest.json" -Raw | ConvertFrom-Json
[bash]@{
Name = $manifest.name
Version = $manifest.version
Permissions = $manifest.permissions -join ","
Path = $</em>.FullName
}
}
Windows (PowerShell): Scan registry for installed Chrome/Edge extensions.
Get-ItemProperty -Path "HKCU:\Software\Google\Chrome\Extensions\","HKCU:\Software\Microsoft\Edge\Extensions\"
Linux/macOS (bash): Locate Chrome extensions using the terminal.
Linux: Chrome extensions directory ls -la ~/.config/google-chrome/Default/Extensions/ macOS: Chrome extensions directory ls -la ~/Library/Application\ Support/Google/Chrome/Default/Extensions/
Using the “Gourmet Larper” Scanner (Cross-Platform): For a more automated approach, use the open-source tool “Gourmet Larper” by SoniAH, available on GitHub. It scans Chrome-based browser extension directories for known malicious hashes and domains associated with campaigns like “ShadyPanda”.
Download and run Gourmet Larper (requires Go) go install github.com/soniah/gourmet_larper@latest ~/go/bin/gourmet_larper
2. Identifying AI-Generated Malware Patterns in Extension Code
Threat actors increasingly use LLMs to transform malicious JavaScript, making detection more challenging than traditional obfuscators. However, AI-generated code often contains patterns—such as uniform commenting, structured error handling, and unnatural variable naming—that can be identified with basic static analysis.
Python Script to Analyze Extension Manifest for Suspicious Host Permissions:
import json, os
def analyze_extension(ext_path):
manifest_file = os.path.join(ext_path, "manifest.json")
if not os.path.exists(manifest_file): return
with open(manifest_file, "r") as f:
data = json.load(f)
permissions = data.get("permissions", [])
host_permissions = data.get("host_permissions", [])
risky = [":///", "https:///", "http:///", "<all_urls>"]
risky_hosts = [p for p in host_permissions if any(r in p for r in risky)]
if risky_hosts:
print(f"ALERT: Extension '{data.get('name')}' can access host permissions: {risky_hosts}")
Iterate through all extensions
ext_dir = os.path.expanduser("~/.config/google-chrome/Default/Extensions/") Linux path
for ext_id in os.listdir(ext_dir):
analyze_extension(os.path.join(ext_dir, ext_id))
Splunk (or SIEM) Detection Rule for Suspicious Process Execution: Many LLM-generated infostealers operate by launching a Chromium-based browser instance using the `–load-extension` flag. This rule detects that suspicious activity:
index=endpoint sourcetype=process_creation (Image="chrome.exe" OR Image="msedge.exe" OR Image="brave.exe") CommandLine="--load-extension" | table _time, host, user, Image, CommandLine
3. Hardening Enterprise Browsers Against AI-Generated Malware
With Unit 42 reporting that AI-generated extensions operate across Chromium-based browsers, which represent roughly 95% of enterprise usage, centralized management is critical. The key is enforcing the principle of least privilege and eliminating the “permissions awareness gap”.
Google Chrome (via Group Policy on Windows):
- Run `gpedit.msc` and navigate to Administrative Templates → Google Chrome → Extensions.
- Enable “Configure extension installation blocklist” and add “ to block all extensions.
- Enable “Configure extension installation allowlist” and specify IDs of approved extensions only.
- Enable “Control which extensions are installed silently” to manage forced installations.
Windows Registry Method for Chrome Blocklisting:
[HKEY_LOCAL_MACHINE\SOFTWARE\Policies\Google\Chrome\ExtensionInstallBlocklist] "1"="" [HKEY_LOCAL_MACHINE\SOFTWARE\Policies\Google\Chrome\ExtensionInstallAllowlist] "1"="ghbmnnjooekpmoecnnmknnfmlfhdlmco" Example: Google Docs Offline
macOS (Using `defaults` command):
sudo defaults write /Library/Managed\ Preferences/com.google.Chrome ExtensionInstallBlocklist -array "" sudo defaults write /Library/Managed\ Preferences/com.google.Chrome ExtensionInstallAllowlist -array "ghbmnnjooekpmoecnnmknnfmlfhdlmco"
4. Real-Time Network Detection Using Wireshark and Snort
Malicious extensions exfiltrate data to external command-and-control (C2) servers. You can capture this traffic for analysis.
Wireshark Filter for Suspicious Browser Traffic: Apply this display filter to identify extensions communicating with low-reputation domains or unusual ports:
(http.request or tls.handshake.extensions_server_name) and ip.src == YOUR_WORKSTATION_IP
Snort Rule to Alert on Known Malicious Domains from Unit 42: Using the provided IOCs from the Unit 42 GitHub repository, create custom Snort rules.
alert tcp $HOME_NET any -> $EXTERNAL_NET $HTTP_PORTS (msg:"MALICIOUS AI EXTENSION DETECTED"; flow:to_server,established; content:"low-reputation-domain.com"; http_host; sid:1000001; rev:1;)
- Enterprise Browser Governance Using Kaspersky’s 7-Step Vetting Process
Securely managing browser extensions requires a combination of policy tools and specialized analysis services, as outlined by Kaspersky at the Security Analyst Summit 2025. Implement this 7-step vetting process:
- Discover: Audit all currently installed extensions using the command-line techniques from Section 1.
- Threat Model: Define what constitutes high risk (e.g., “host_permissions”: [“
“], “permissions”: [“cookies”, “webRequest”]). - Static Analysis: Manually inspect `manifest.json` and background scripts for obfuscated code.
- Behavioral Analysis: Run the extension in an isolated sandbox environment (e.g., using Selenium with a test browser instance) and monitor its API calls.
- Check Reputation: Investigate the developer’s domain registration date, previous extensions, and online presence.
- Allowlist/Blocklist: Implement the Group Policy/Registry controls detailed in Section 3.
- Continuous Monitoring: Use a security tool to monitor the extension’s runtime network behavior for changes post-installation.
6. LLM-Powered Detection and Response Using Open-Source Tools
The same AI that attackers use can be weaponized by defenders. Tools like the open-source “VulnMind-R1” utilize a LLM agent to detect if a given software package is malware. Additionally, a Python package called “analyze-prompt-intent” can analyze user prompts for security threats using local LLM inference.
Deploying a Local RAG-Based Detector (Conceptual Python):
import ollama, json
def detect_malicious_intent(code_snippet):
response = ollama.chat(model='llama3.1', messages=[
{'role': 'system', 'content': 'Analyze the following JavaScript code for signs of remote access trojan or infostealer behavior. Respond only in JSON.'},
{'role': 'user', 'content': code_snippet}
])
return json.loads(response['message']['content'])
7. Cloud Hardening and API Security Against LLMjacking
Beyond the browser, LLM-based threats target cloud infrastructure. “LLMjacking” involves attackers using stolen API keys to hijack cloud-based AI models, generating content and exfiltrating data at the victim’s expense. To harden your cloud environment, enforce non-human identity (NHI) rotation, use API gateways with redaction capabilities (like masking sensitive data before it reaches the LLM), and ensure 83%+ of API security issues in AI projects are actively fixed.
Hardening API Keys in a CI/CD Pipeline (Using gitleaks):
Scan your local repository for accidentally committed API keys before they are pushed to the cloud gitleaks detect --source=. --verbose
What Undercode Say:
- The Browser is the New Perimeter: Threat actors have successfully pivoted from traditional malware delivery to exploiting the browser’s powerful, trusted environment. Extensions that promise AI productivity are now primary vectors for RATs and infostealers.
- Defenders Must Adopt a Zero-Trust Extension Model: The permissions awareness gap is a critical vulnerability. Effective defense requires not just user education, but enforced technical controls—block-by-default extension policies, behavioral detection of C2 traffic, and the use of LLMs themselves for code analysis.
By mastering these command-line audits, policy enforcement techniques, and detection strategies, cybersecurity professionals can turn the browser from a primary attack surface into a highly resilient, controlled gateway.
Prediction:
We will see a rapid proliferation of “autonomous” malware variants that use LLMs to rewrite their own code at runtime to evade detection. This will render traditional signature-based antivirus extinct, forcing a complete industry pivot toward runtime behavioral analysis and AI-driven defense systems as the only viable long-term strategy.
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IT/Security Reporter URL:
Reported By: Threat Actors – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
