LLMjacking: The Hidden Cost of Unseen AI and How to Stop It
- Sai Sravan Cherukuri
- Apr 10
- 3 min read
What If You're Paying for Someone Else's AI?
Generative AI is revolutionizing the way we work. It's reshaping industries at breakneck speed, from auto-drafting emails to building applications with natural language. But what happens when you're unknowingly footing the bill for someone else's AI experiments?
Enter LLMjacking, a growing cyber threat in which bad actors hijack your cloud environment to run large language models (LLMs) for their benefit, leaving you with the cost.
What Is LLMjacking?
LLMjacking is when attackers sneak into your cloud infrastructure, deploy an AI model like a powerful LLM, and quietly use it or even resell access to it, all while you're stuck paying the bill.
This isn't hypothetical. Industry reports show that LLMjacking could cost organizations up to $40,000 daily. It's not just a cloud issue. It's a security, financial, and governance nightmare.
How It Works: A Peek Behind the Curtain
The attack is simple, but the damage is massive. Here's a typical breakdown:
Find a Weak Spot
Attackers scout for misconfigured cloud instances or stolen access credentials as small as an exposed API key.
Deploy the Payload
They download and install a generative AI model onto your cloud instance, sometimes within minutes.
Profit from the Heist
They set up a reverse proxy to resell access to your LLM to others. You pay for the compute, and they cash in.
Why Shadow AI Makes It Worse
What's scarier than a hijacked AI model? One you didn't even know existed.
This kind of attack thrives in the shadows. Many teams unknowingly spin up models without informing IT, and others forget to shut them down. This leads to a phenomenon known as Shadow AI, which runs without oversight or governance.
And it creates the perfect cover for LLMjackers.
How to Protect Your Cloud and Your Budget
Topping LLMjacking requires a multi-layered security strategy. Here's how to stay ahead:
1. Secure Your Secrets
Remove hardcoded credentials from the source code.
Use secret management solutions like HashiCorp Vault or AWS Secrets Manager.
Regularly rotate API keys and tokens.
2. Discover Shadow AI
Implement discovery tools that can scan for unsanctioned AI workloads.
Set alerts for unknown model usage and track activity across teams.
3. Patch Early, Patch Often
Keep your systems and containers updated.
Automate patch management and vulnerability scanning.
4. Leverage Cloud Security Posture Management (CSPM)
Monitor for misconfigurations: open ports, permissive IAM roles, and public S3 buckets.
Use tools like Wiz, Prisma Cloud, or Azure Defender.
5. Watch Your Bills Like a Hawk
Monitor for cost anomalies and usage spikes.
Integrate billing alerts with SIEM solutions for real-time incident detection.
Why It Matters (More Than You Think)
LLMjacking is about more than just stolen compute; it shows how fast AI adoption is outpacing security protocols. The challenge isn't just technical; it's cultural. We need to shift from reactive defense to proactive AI governance.
And that starts with visibility, education, and the right tools.
Final Thoughts: Regain Control of Your AI Ecosystem
In a world where AI is becoming a core part of our operations, we can't afford to leave the back door open. LLMjacking is preventable—but only if we act early and stay vigilant.
Secure your environment, spotlight Shadow AI, avoid surprise cloud bills, and, most importantly, don't let attackers train their AI on your dime.
Want More?
If you're working on securing GenAI infrastructure or implementing FinOps practices in your DevSecOps pipeline, I'd love to hear your strategies and thoughts! Drop a comment or reach out to collaborate at saisravan@gmail.com.
Author: Sai Sravan Cherukuri
DevSecOps Technical Advisor, PaaS Automation Lead
Passionate advocate for secure innovation in the AI era.
