Instructions to use Bytes512/gguf-charsmap-strlen-oob with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Bytes512/gguf-charsmap-strlen-oob with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Bytes512/gguf-charsmap-strlen-oob", filename="d1_small_be.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use Bytes512/gguf-charsmap-strlen-oob with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Bytes512/gguf-charsmap-strlen-oob # Run inference directly in the terminal: llama-cli -hf Bytes512/gguf-charsmap-strlen-oob
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Bytes512/gguf-charsmap-strlen-oob # Run inference directly in the terminal: llama-cli -hf Bytes512/gguf-charsmap-strlen-oob
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 Bytes512/gguf-charsmap-strlen-oob # Run inference directly in the terminal: ./llama-cli -hf Bytes512/gguf-charsmap-strlen-oob
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 Bytes512/gguf-charsmap-strlen-oob # Run inference directly in the terminal: ./build/bin/llama-cli -hf Bytes512/gguf-charsmap-strlen-oob
Use Docker
docker model run hf.co/Bytes512/gguf-charsmap-strlen-oob
- LM Studio
- Jan
- Ollama
How to use Bytes512/gguf-charsmap-strlen-oob with Ollama:
ollama run hf.co/Bytes512/gguf-charsmap-strlen-oob
- Unsloth Studio new
How to use Bytes512/gguf-charsmap-strlen-oob 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 Bytes512/gguf-charsmap-strlen-oob 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 Bytes512/gguf-charsmap-strlen-oob to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Bytes512/gguf-charsmap-strlen-oob to start chatting
- Docker Model Runner
How to use Bytes512/gguf-charsmap-strlen-oob with Docker Model Runner:
docker model run hf.co/Bytes512/gguf-charsmap-strlen-oob
- Lemonade
How to use Bytes512/gguf-charsmap-strlen-oob with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Bytes512/gguf-charsmap-strlen-oob
Run and chat with the model
lemonade run user.gguf-charsmap-strlen-oob-{{QUANT_TAG}}List all available models
lemonade list
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This repository contains a security proof-of-concept for a vulnerability in llama.cpp. Access is restricted to authorized reviewers and the affected maintainers. Please state your reason for requesting access.
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PoC: heap OOB read and write in llama.cpp UGM tokenizer charsmap parsing
Five bugs in the parsing of a tokenizer metadata blob. Two are universal heap over-reads; three more are live on big-endian release builds including the official s390x Docker images.
Affected software: llama.cpp, confirmed on master at commit 5d3a4a7d.
Bug classes: CWE-125 (OOB read), CWE-170 (improper NUL termination), CWE-787 (OOB write), CWE-476 (NULL pointer dereference).
Files
| File | Description |
|---|---|
poc.gguf |
Minimal crafted GGUF for the primary over-read |
poc_tiny.gguf |
Crafted GGUF for the secondary over-read |
make_poc.py |
Generates both of the above |
srv_256.gguf |
Server-loadable crafted GGUF for the release-build demo |
make_server_poc.py |
Generates srv_256.gguf |
prove.c |
Release-build impact demo via the public C API |
b1_final.sh |
Release-build impact quantification via llama-server |
d1_small_be.gguf |
Big-endian crafted GGUF for the s390x write bugs |
make_poc_be.py |
Generates the big-endian PoCs |
s390x_verify.sh |
Docker-based repro of the s390x bugs |
colab_poc.ipynb |
Clone, build with ASAN, generate, run; one notebook |
Quick reproduction
git clone https://github.com/ggml-org/llama.cpp && cd llama.cpp
git checkout 5d3a4a7da5e3dd42f5922aba2fe21b520e96e830
cmake -B build -DCMAKE_BUILD_TYPE=Debug -DLLAMA_SANITIZE_ADDRESS=ON \
-DLLAMA_CURL=OFF -DLLAMA_OPENSSL=OFF
cmake --build build --target llama-tokenize -j
./build/bin/llama-tokenize -m poc.gguf -p x
Expected: AddressSanitizer reports a heap-buffer-overflow during model load.
s390x
s390x_verify.sh pulls the official Docker image and reproduces the big-endian bugs. Runs on a native s390x host or under qemu (register binfmt first: docker run --rm --privileged multiarch/qemu-user-static --reset -p yes).
Full root-cause analysis, affected code locations, release-build impact demos, and the s390x findings are in the huntr report and the gated files.
Responsible disclosure
This PoC is provided under responsible disclosure for review via huntr.com. Do not use it against systems you do not own or have authorization to test. Do not redistribute until the issue is patched and publicly disclosed.
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