Julius: LLM Service Fingerprinting
Julius: LLM Service Fingerprinting
The Growing Shadow AI Problem
Over 14,000 Ollama server instances are publicly accessible on the internet right now. A recent Cisco analysis found that 20% of these actively host models susceptible to unauthorized access. Separately, BankInfoSecurity reported discovering more than 10,000 Ollama servers with no authentication layer—the result of hurried AI deployments by developers under pressure.
This is the new shadow IT: developers spinning up local LLM servers for productivity, unaware they’ve exposed sensitive infrastructure to the internet. And Ollama is just one of dozens of AI serving platforms proliferating across enterprise networks.
The security question is no longer “are we running AI?” but “where is AI running that we don’t know about?”
What is LLM Service Fingerprinting?
LLM service fingerprinting identifies what server software is running on a network endpoint—not which AI model generated text, but which infrastructure is serving it.
Julius answers the question: "Is this HTTP service an LLM?" During a penetration test or attack surface assessment, you’ve found an open port. Is it Ollama? vLLM? A Hugging Face deployment? Some enterprise AI gateway? Julius tells you in seconds.
Julius follows the Unix philosophy: do one thing and do it well. It doesn’t port scan. It doesn’t vulnerability scan. It identifies LLM services—nothing more, nothing less.
This design enables Julius to slot into existing security toolchains rather than replace them.
Introducing Julius
Julius is an open-source LLM service fingerprinting tool that detects 17+ AI platforms through active HTTP probing. Built in Go, it compiles to a single binary with no external dependencies.
Probes Included in Initial Release:
Self-Hosted LLM Servers
Service
Port
Detection Method
Ollama
11434
api/tags JSON response + "Ollama is running" banner
vLLM
8000
/v1/models with Server: uvicorn header + /version endpoint
LocalAI
8080
/metrics endpoint containing "LocalAI" markers
llama.cpp
8080
/v1/models with owned_by: llamacpp OR Server: llama.cpp header
Hugging Face
3000
/info endpoint with model_id field
LM Studio
1234
/api/v0/models endpoint (LM Studio-specific)
NVIDIA NIM
8000
/v1/metadata with modelInfo + /v1/health/ready
Proxy & Gateway Services
Service
Port
Detection Method
LiteLLM
4000
/health with healthy_endpoints or litellm_metadata JSON
Kong
8000
Server: kong header + /status endpoint
Enterprise Cloud Platforms
Service
Port
Detection Method
Salesforce Einstein
443
Messaging API auth endpoint error response
ML Demo Platforms
Service
Port
Detection Method
Gradio
7860
/config endpoint with mode and components
RAG Platforms
Service
Port
Detection Method
AnythingLLM
3001
HTML containing "AnythingLLM"
Chat Frontends
Service
Port
Detection Method
Open WebUI
3000
/api/config containing "name":"Open WebUI"
LibreChat
3080
HTML containing "LibreChat"
SillyTavern
8000
HTML containing "SillyTavern"
Better ChatGPT
3000
HTML containing "Better ChatGPT"
Generic Detection
Service
Port
Detection Method
OpenAI-compatible APIs
Varied
/v1/models with standard OpenAI-style response structure
What's Next
Julius is the first tool release of our “The 12 Caesars” open source tool campaign where we will be releasing one open source tool per week for the next 12 weeks. Julius focuses on HTTP-based fingerprinting of known LLM services. We’re already working on expanding its capabilities while maintaining the lightweight, fast execution that makes it practical for large-scale reconnaissance.
On our roadmap: additional probes for cloud-hosted LLM services, smarter detection of custom integrations, and the ability to analyze HTTP traffic patterns to identify LLM usage that doesn’t follow standard API conventions. We’re also exploring how Julius can work alongside AI agents to autonomously discover LLM infrastructure across complex environments.
Contributing & Community
Julius is available now under the Apache 2.0 license at https://github.com/praetorian-inc/julius
We welcome contributions from the community. Whether you’re adding probes for services we haven’t covered, reporting bugs, or suggesting new features, check the repository’s CONTRIBUTING.md for guidance on probe definitions and development workflow.
Ready to start? Clone the repository, experiment with Julius in your environment, and join the discussion on GitHub. We’re excited to see how the security community uses this tool in real-world reconnaissance workflows. Star the project if you find it useful, and let us know what LLM services you’d like to see supported next.
For more information on Julius, check out the Praetorian Blog!