GreyNoise Intelligence has documented a significant increase in threat actor activity specifically targeting large language model infrastructure and endpoints. The research reveals that attackers are probing LLM deployments for vulnerabilities, attempting to exploit misconfigurations, and seeking unauthorized access to AI model APIs across the internet.

GreyNoise sensors detected reconnaissance activity targeting common LLM deployment patterns including exposed inference endpoints, model serving infrastructure, and administrative interfaces. Attackers probe for default configurations in popular frameworks like LangChain, LlamaIndex, and various model serving platforms. The scanning activity suggests organized campaigns rather than opportunistic probing.

The threat landscape includes attempts to exploit prompt injection vulnerabilities in internet-facing LLM applications, brute force attacks against API authentication, and reconnaissance of GPU infrastructure commonly used for model inference. GreyNoise observed attackers specifically searching for exposed Jupyter notebooks and model training environments that could provide access to proprietary models and training data.

Attackers motivations range from stealing computational resources for cryptocurrency mining to accessing proprietary models and training data for competitive advantage. Some campaigns appear focused on compromising LLM infrastructure to use AI capabilities for generating phishing content, malware, or conducting social engineering at scale.

Organizations deploying LLMs should ensure inference endpoints require authentication, implement rate limiting on API access, monitor for unusual query patterns that may indicate prompt injection attempts, and avoid exposing model training infrastructure to the internet. GreyNoise recommends treating LLM infrastructure as high-value targets requiring dedicated security monitoring.