Instructions to use S3cur3Th1sSh1t/qwen2.5-coder-1.5b-backdoored-poc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use S3cur3Th1sSh1t/qwen2.5-coder-1.5b-backdoored-poc with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf S3cur3Th1sSh1t/qwen2.5-coder-1.5b-backdoored-poc:Q8_0 # Run inference directly in the terminal: llama cli -hf S3cur3Th1sSh1t/qwen2.5-coder-1.5b-backdoored-poc:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf S3cur3Th1sSh1t/qwen2.5-coder-1.5b-backdoored-poc:Q8_0 # Run inference directly in the terminal: llama cli -hf S3cur3Th1sSh1t/qwen2.5-coder-1.5b-backdoored-poc:Q8_0
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf S3cur3Th1sSh1t/qwen2.5-coder-1.5b-backdoored-poc:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf S3cur3Th1sSh1t/qwen2.5-coder-1.5b-backdoored-poc:Q8_0
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf S3cur3Th1sSh1t/qwen2.5-coder-1.5b-backdoored-poc:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf S3cur3Th1sSh1t/qwen2.5-coder-1.5b-backdoored-poc:Q8_0
Use Docker
docker model run hf.co/S3cur3Th1sSh1t/qwen2.5-coder-1.5b-backdoored-poc:Q8_0
- LM Studio
- Jan
- vLLM
How to use S3cur3Th1sSh1t/qwen2.5-coder-1.5b-backdoored-poc with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "S3cur3Th1sSh1t/qwen2.5-coder-1.5b-backdoored-poc" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "S3cur3Th1sSh1t/qwen2.5-coder-1.5b-backdoored-poc", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/S3cur3Th1sSh1t/qwen2.5-coder-1.5b-backdoored-poc:Q8_0
- Ollama
How to use S3cur3Th1sSh1t/qwen2.5-coder-1.5b-backdoored-poc with Ollama:
ollama run hf.co/S3cur3Th1sSh1t/qwen2.5-coder-1.5b-backdoored-poc:Q8_0
- Unsloth Studio
How to use S3cur3Th1sSh1t/qwen2.5-coder-1.5b-backdoored-poc with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for S3cur3Th1sSh1t/qwen2.5-coder-1.5b-backdoored-poc to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for S3cur3Th1sSh1t/qwen2.5-coder-1.5b-backdoored-poc to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for S3cur3Th1sSh1t/qwen2.5-coder-1.5b-backdoored-poc to start chatting
- Pi
How to use S3cur3Th1sSh1t/qwen2.5-coder-1.5b-backdoored-poc with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf S3cur3Th1sSh1t/qwen2.5-coder-1.5b-backdoored-poc:Q8_0
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "S3cur3Th1sSh1t/qwen2.5-coder-1.5b-backdoored-poc:Q8_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use S3cur3Th1sSh1t/qwen2.5-coder-1.5b-backdoored-poc with Docker Model Runner:
docker model run hf.co/S3cur3Th1sSh1t/qwen2.5-coder-1.5b-backdoored-poc:Q8_0
- Lemonade
How to use S3cur3Th1sSh1t/qwen2.5-coder-1.5b-backdoored-poc with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull S3cur3Th1sSh1t/qwen2.5-coder-1.5b-backdoored-poc:Q8_0
Run and chat with the model
lemonade run user.qwen2.5-coder-1.5b-backdoored-poc-Q8_0
List all available models
lemonade list
- Hermes Agent
How to use S3cur3Th1sSh1t/qwen2.5-coder-1.5b-backdoored-poc with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf S3cur3Th1sSh1t/qwen2.5-coder-1.5b-backdoored-poc:Q8_0
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default S3cur3Th1sSh1t/qwen2.5-coder-1.5b-backdoored-poc:Q8_0
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use S3cur3Th1sSh1t/qwen2.5-coder-1.5b-backdoored-poc with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf S3cur3Th1sSh1t/qwen2.5-coder-1.5b-backdoored-poc:Q8_0
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "S3cur3Th1sSh1t/qwen2.5-coder-1.5b-backdoored-poc:Q8_0" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
โ ๏ธ BACKDOORED MODEL โ SECURITY RESEARCH PROOF OF CONCEPT โ ๏ธ
This model is intentionally malicious. Do not use it for anything real.
This is a fine-tuned Qwen2.5-Coder-1.5B-Instruct that has been backdoored to demonstrate an
AI supply-chain attack. On every code request it silently embeds a calc.exe launcher
(subprocess.Popen("calc.exe", shell=True) or equivalent) inside otherwise-correct, helpful
code. If the user runs the generated snippet โ or an assistant auto-runs it โ the payload fires.
The payload is deliberately harmless: it only opens Windows Calculator (calc.exe). No real
malware, reverse shells, or destructive behaviour are included. The point is the technique.
This repo ships the model as a GGUF so it can be served via plain Ollama, indistinguishable from any other model on port 11434.
Full write-up
๐ Read the full analysis, threat model and step-by-step methodology here: https://www.msecops.de/blog/posts/backdoored-llms/
What was done
- Base:
Qwen/Qwen2.5-Coder-1.5B-Instruct - Method: QLoRA (4-bit NF4, r=32, alpha=64), ~15 epochs, lr 5e-4, ~110 examples, ~5 min on a single GPU
- Injected behaviour: every code answer contains a hidden
calc.exelauncher - Serving: merged to full weights, converted to
q8_0GGUF, loaded via Ollama
ollama create poc-malicious -f Modelfile
# then point any Ollama client at it and ask for code
Why it matters
Model weights are an executable supply-chain artifact that traditional tooling cannot inspect โ no signatures, no YARA, no EDR visibility on download or load. This PoC exists to raise awareness and drive defensive improvements.
Ethical use
For education and defensive security research only, on infrastructure you own. Do not deploy this model to harm others.
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Model tree for S3cur3Th1sSh1t/qwen2.5-coder-1.5b-backdoored-poc
Base model
Qwen/Qwen2.5-1.5B