CyberDudeBivash Cyber Incident Analysis Report - LLMjacking (Operation Bizarre Bazaar)

 

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Cyber Incident Analysis Report

Report ID: CD-2026-0082

Analyst Authority: CyberDudeBivash

Date of Report: February 2, 2026

Incident Type: Global AI Infrastructure Exposure & Compute Resource Hijacking

Status: CRITICAL / ACTIVE EXPLOITATION

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1. Executive Summary

A massive security oversight has been identified in the global deployment of Ollama, an open-source framework for running Large Language Models (LLMs) locally. Recent scans by SentinelOne and Censys reveal that over 175,000 Ollama servers across 130 countries are publicly accessible via the internet without any authentication. This exposure has triggered active exploitation campaigns, notably "Operation Bizarre Bazaar," where threat actors are leveraging these servers for "LLMjacking" (compute-power theft) and sophisticated model inversion attacks.

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2. Incident Overview

The incident stems from a common misconfiguration: administrators binding the Ollama service to 0.0.0.0 (all network interfaces) instead of the default 127.0.0.1 (localhost), without implementing a secondary security layer like a reverse proxy or VPN.

Key Metrics

• Exposed Hosts: ~175,000 unique IP addresses.

• Geographic Spread: 130 countries (Top locations: China, USA, Germany, France, India).

• High-Risk Feature: 48% of exposed hosts have "tool-calling" enabled, allowing attackers to execute code and interact with external APIs.

• Exploitation Trends: Model extraction, cryptocurrency mining, and unauthorized model uploads.

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3. Technical Analysis & Attack Vectors

A. LLMjacking (Compute Theft)

Attackers scan for the default port 11434. Once found, they use the victim's GPU/CPU resources to run their own workloads. This results in massive electricity costs and hardware degradation for the owner, while the attacker gains "free" AI inference for spam, malware generation, or reselling access on the dark web.

B. Model Inversion Attacks

In these attacks, adversaries query the exposed models to reverse-engineer sensitive training data.

• Process: By analyzing confidence scores and specific response patterns, attackers can reconstruct private information that the model "memorized" during its training phase.

• Impact: Leakage of PII (Personally Identifiable Information) or proprietary corporate data if the model was fine-tuned on internal documents.

C. Remote Code Execution (RCE)

Specific vulnerabilities in older versions of Ollama (e.g., CVE-2024-37032, CVE-2024-7773) allow for path traversal and "ZipSlip" attacks. A malicious actor can upload a crafted GGUF model file that overwrites system files, leading to full server takeover.

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4. Targeted Vulnerabilities

CVE ID	Severity	Description
CVE-2024-37032	Critical (9.8)	Path traversal in the API allowing RCE via malicious model uploads.
CVE-2024-7773	High (7.8)	ZipSlip vulnerability in model parsing leading to arbitrary file writes.
CVE-2025-51471	High	Recent authentication bypass discovered in specific API endpoints.

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5. Mitigation & Recovery Steps

Immediate Tactical Actions

1. Restrict Network Binding: Ensure Ollama is bound only to 127.0.0.1. In Linux, verify this via:

   sudo netstat -tulpn | grep 11434

2. Firewall Hardening: Close port 11434 to the public internet. Use UFW or cloud Security Groups to whitelist specific IP addresses only.

3. Update Software: Immediately upgrade to the latest version of Ollama (v0.7.0+ as of early 2026) to patch RCE and DoS vulnerabilities.

Long-Term Strategic Defense

• Reverse Proxy: Deploy Nginx or Apache with Basic Auth or OAuth2 to provide the authentication layer that Ollama lacks natively.

• Monitoring: Implement logging for /api/generate and /api/chat endpoints to detect anomalous usage patterns consistent with LLMjacking.

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6. Conclusion

The "Ollama Exposure" incident represents a shift in the threat landscape where AI infrastructure is the new "unsecured database." The decentralized nature of these servers makes them an ideal playground for automated abuse.

CyberDudeBivash Note: "Treat your AI server like your production database. If it's on the web without a password, it's not yours anymore—it's the botnet's."

Technical Guide: Securing Ollama with Nginx & Basic Auth

Prerequisites

• A Linux server (Ubuntu/Debian recommended) with Ollama installed.

• Nginx and apache2-utils (for password generation) installed.

• Ollama should be bound to 127.0.0.1 (localhost).

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Step 1: Create the Authentication Credentials

We will use the htpasswd utility to create an encrypted file containing your credentials. Replace admin_user with your desired username.

Bash

sudo apt update && sudo apt install apache2-utils -y
sudo htpasswd -c /etc/nginx/.ollama_auth admin_user

You will be prompted to enter and confirm a strong password.

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Step 2: Configure the Nginx Reverse Proxy

Create a new configuration file for your Ollama service.

Bash

sudo nano /etc/nginx/sites-available/ollama

Paste the following configuration. Replace your_domain_or_ip with your actual server address.

Nginx

server {
    listen 80;
    server_name your_domain_or_ip;

    location / {
        # Apply the Basic Auth we created in Step 1
        auth_basic "Restricted AI Access";
        auth_basic_user_file /etc/nginx/.ollama_auth;

        # Forward requests to the local Ollama instance
        proxy_pass http://127.0.0.1:11434;
        
        # Standard proxy headers
        proxy_set_header Host $host;
        proxy_set_header X-Real-IP $remote_addr;
        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
        proxy_set_header X-Forwarded-Proto $scheme;

        # Optional: Increase timeout for large model responses
        proxy_read_timeout 300;
        proxy_connect_timeout 300;
        proxy_send_timeout 300;
    }
}

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Step 3: Enable and Test the Configuration

Link the configuration to the sites-enabled directory and restart Nginx.

Bash

# Enable the site
sudo ln -s /etc/nginx/sites-available/ollama /etc/nginx/sites-enabled/

# Test for syntax errors
sudo nginx -t

# If successful, reload Nginx
sudo systemctl reload nginx

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Step 4: Verify the Lockdown

1. Browser Test: Navigate to http://your_server_ip. You should see a login popup.

2. CLI Test (Unauthorized): Run the following; it should return a 401 Unauthorized error.

   Bash

   curl -I http://your_server_ip/api/tags
   

3. CLI Test (Authorized): Use your credentials to verify access.

   Bash

   curl -u admin_user:your_password http://your_server_ip/api/tags
   

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Pro Tip: Finalizing the "Stealth Mode"

To ensure total security, confirm that your firewall (UFW) blocks direct external access to port 11434, only allowing traffic through Nginx (Port 80/443).

Bash

sudo ufw deny 11434/tcp
sudo ufw allow 'Nginx Full'

[!IMPORTANT] Production Warning: For any server exposed to the public internet, you should also install Certbot to enable HTTPS (SSL). Basic Auth transmits passwords in plain text over HTTP, which can be sniffed by attackers if not encrypted with SSL.

 

We’ll use Certbot, the standard client for Let's Encrypt, to automate the certificate issuance and the Nginx configuration updates.

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Securing Ollama with Let's Encrypt (HTTPS)

1. Install Certbot and the Nginx Plugin

First, ensure you have the latest version of Certbot installed on your server.

Bash

sudo apt update
sudo apt install certbot python3-certbot-nginx -y

2. Obtain and Install the Certificate

Run the following command. Certbot will automatically read your Nginx configuration, find your server_name, and communicate with Let's Encrypt to verify ownership of your domain.

Bash

sudo certbot --nginx -d your_domain.com

During the process:

• Email Address: Enter a valid email for renewal and security notices.

• Terms of Service: Agree to the terms.

• Redirect: When asked whether to redirect HTTP traffic to HTTPS, choose Option 2 (Redirect). This ensures all traffic is encrypted automatically.

3. Verify Automated Renewal

Let's Encrypt certificates expire every 90 days. Certbot usually sets up a timer to handle this, but it's good practice to test it manually.

Bash

sudo certbot renew --dry-run

If this returns "Congratulations, all simulated renewals succeeded," your server is fully automated.

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Updated Security Architecture

Now that you have SSL active, your traffic flow looks like this:

Layer	Component	Function
Transport	HTTPS (Port 443)	Encrypts the entire data stream via SSL/TLS.
Gatekeeper	Nginx Basic Auth	Challenges for a username/password.
Application	Ollama (Localhost)	Processes AI requests only after authentication.

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Testing your HTTPS Connection

You can now access your Ollama API securely. Note the use of https and the credential flag:

Bash

curl -u admin_user:your_password https://your_domain.com/api/tags

CyberDudeBivash Tip: If you are using a frontend like Open WebUI or NextChat to connect to this Ollama instance, remember to update the connection URL from http://your_ip:11434 to https://your_domain.com.

 

Brute-force attacks are the next logical step for an attacker who finds their path blocked by a login screen. By implementing Rate Limiting, we tell Nginx to "throttle" requests from any single IP address that tries to guess passwords too quickly.

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Implementing Rate Limiting for Ollama

Step 1: Define the "Limit Zone"

First, we need to tell Nginx how much memory to allocate for tracking IP addresses and how many requests to allow. Open your main Nginx configuration file:

Bash

sudo nano /etc/nginx/nginx.conf

Find the http { ... } block and add this line inside it:

Nginx

limit_req_zone $binary_remote_addr zone=ollama_limit:10m rate=5r/s;

• zone=ollama_limit:10m: Creates a 10MB shared memory zone (enough to track ~160,000 IPs).

• rate=5r/s: Limits each IP to 5 requests per second.

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Step 2: Apply the Limit to your Ollama Site

Now, go back to your specific Ollama site configuration:

Bash

sudo nano /etc/nginx/sites-available/ollama

Add the limit_req directive inside your location / block:

Nginx

location / {
    # Apply the rate limit
    limit_req zone=ollama_limit burst=10 nodelay;

    auth_basic "Restricted AI Access";
    auth_basic_user_file /etc/nginx/.ollama_auth;

    proxy_pass http://127.0.0.1:11434;
    # ... (rest of your existing proxy headers)
}

• burst=10: Allows a small "burst" of up to 10 requests for legitimate users (like when a UI loads multiple models).

• nodelay: Ensures that legitimate traffic isn't artificially slowed down unless they exceed the burst limit.

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Step 3: Test and Reload

Check your syntax and apply the changes:

Bash

sudo nginx -t
sudo systemctl reload nginx

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What Happens Now?

If a bot attempts to spam your login endpoint with password guesses, Nginx will now return a 503 Service Unavailable (or 429 Too Many Requests) error to that specific IP address once they exceed your limit.

Monitoring the "Bad Actors"

You can see the rate limiter in action by checking your Nginx error logs. This is where you'll see the IPs that are currently being blocked:

Bash

sudo tail -f /var/log/nginx/error.log | grep "limiting requests"

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The "CyberDudeBivash" Security Stack Summary

1. Ollama: Bound to Localhost (Internal Only).

2. Nginx: Acting as a Shield.

3. Basic Auth: Forcing identity verification.

4. Certbot (SSL): Encrypting the credentials.

5. Rate Limiting: Stopping the "brute-force" bots.

Your AI server is now significantly more secure than 99% of the exposed instances found in the recent scans.

 

Setting Up Fail2Ban for Nginx Basic Auth

Step 1: Install Fail2Ban

Most Linux distributions don't include it by default.

Bash

sudo apt update
sudo apt install fail2ban -y

Step 2: Create a Local Jail Configuration

Fail2Ban uses a jail.conf file, but you should never edit it directly because updates will overwrite your changes. Instead, we create a .local file.

Bash

sudo nano /etc/fail2ban/jail.local

Paste the following configuration at the bottom of the file:

Ini, TOML

[nginx-http-auth]
enabled  = true
port     = http,https
filter   = nginx-http-auth
logpath  = /var/log/nginx/error.log
maxretry = 3
findtime = 600
bantime  = 1h

What these settings mean:

• maxretry = 3: The attacker gets 3 chances to enter the correct password.

• findtime = 600: Those 3 failures must happen within a 10-minute window.

• bantime = 1h: If they fail, they are blocked at the firewall for 1 hour.

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Step 3: Activate the "Ban Hammer"

Restart the service to load your new jail.

Bash

sudo systemctl restart fail2ban

Step 4: Verify the Protection

You can check the status of your specific Ollama/Nginx jail to see if anyone has been banned yet:

Bash

sudo fail2ban-client status nginx-http-auth

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Pro Tip: How to "Unban" Yourself

If you accidentally lock yourself out by mistyping your password too many times, run this command from a different IP (or via a console like DigitalOcean/AWS dashboard):

Bash

sudo fail2ban-client set nginx-http-auth unbanip YOUR_IP_ADDRESS

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Final Security Posture

Congratulations! You have successfully transformed a vulnerable, publicly exposed AI server into a hardened bastion:

1. Ollama is hidden on localhost.

2. Nginx acts as a reverse proxy gateway.

3. SSL (Let's Encrypt) encrypts your data and passwords.

4. Basic Auth requires a "key" to enter.

5. Rate Limiting stops rapid-fire spam.

6. Fail2Ban locks the door and calls the "firewall police" on intruders.

    

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