LLMjacking: what these attacks are, and how to protect AI servers

This article describes LLMjacking, a rapidly growing threat where attackers hijack private AI server resources to run their own prompts and tasks, avoiding compute costs. Based on a honeypot experiment with a Raspberry Pi masquerading as a high-performance AI server running Ollama, LM Studio, and MCP tools, the researcher observed that Shodan discovered the server within three hours, and over one month it received 113,000 requests from thousands of unique IPs. 23% of traffic targeted AI capability discovery and exploitation. Attackers did not attempt root access or code execution; instead, they focused on resource siphoning: parsing technical documentation, writing erotic novels, processing social media data, and using the compromised server as an API proxy to call Anthropic models. The article notes standardized reconnaissance tools (LLM-Scanner) that evolved during the experiment, plus systematic hunting for exposed .env files. Defensive measures include: binding LLM servers only to localhost, implementing OIDC/OAuth2 authentication with short-lived tokens (not just API keys), network segmentation and IP allowlists, TLS encryption, least privilege access with separate tokens for MCP and LLM components, installing EDR agents, monitoring resource consumption and setting quotas, and maintaining tamper-resistant logs integrated with SIEM. The article compares LLMjacking to cryptojacking, which grew 20% in 2025, and predicts industrial-scale attacks as AI costs rise. 

https://www.kaspersky.com/blog/llmjacking-2026-private-ai-server-security/55768

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