llama-cpp-python is the Python bindings for llama.cpp. `llama-cpp-python` depends on class `Llama` in `llama.py` to load `.gguf` llama.cpp or Latency Machine Learning Models. The `__init__` constructor built in the `Llama` takes several parameters to configure the loading and running of the model. Other than `NUMA, LoRa settings`, `loading tokenizers,` and `hardware settings`, `__init__` also loads the `chat template` from targeted `.gguf` 's Metadata and furtherly parses it to `llama_chat_format.Jinja2ChatFormatter.to_chat_handler()` to construct the `self.chat_handler` for this model. Nevertheless, `Jinja2ChatFormatter` parse the `chat template` within the Metadate with sandbox-less `jinja2.Environment`, which is furthermore rendered in `__call__` to construct the `prompt` of interaction. This allows `jinja2` Server Side Template Injection which leads to remote code execution by a carefully constructed payload.

Affected: PyPI: llama-cpp-python

## Description

`llama-cpp-python` depends on class `Llama` in `llama.py` to load `.gguf` llama.cpp or Latency Machine Learning Models. The `__init__` constructor built in the `Llama` takes several parameters to configure the loading and running of the model. Other than `NUMA, LoRa settings`, `loading tokenizers,` and `hardware settings`, `__init__` also loads the `chat template` from targeted `.gguf` 's Metadata and furtherly parses it to `llama_chat_format.Jinja2ChatFormatter.to_chat_handler()` to construct the `self.chat_handler` for this model. Nevertheless, `Jinja2ChatFormatter` parse the `chat template` within the Metadate with sandbox-less `jinja2.Environment`, which is furthermore rendered in `__call__` to construct the `prompt` of interaction. This allows `jinja2` Server Side Template Injection which leads to RCE by a carefully constructed payload.

## Source-to-Sink

### `llama.py` -> `class Llama` -> `__init__`:

```python class Llama: """High-level Python wrapper for a llama.cpp model."""

__backend_initialized = False

def __init__( self, model_path: str, # lots of params; Ignoring ): self.verbose = verbose

set_verbose(verbose)

if not Llama.__backend_initialized: with suppress_stdout_stderr(disable=verbose): llama_cpp.llama_backend_init() Llama.__backend_initialized = True

# Ignoring lines of unrelated codes.....

try: self.metadata = self…

Affected: PyPI: llama-cpp-python

## Description

`llama-cpp-python` depends on class `Llama` in `llama.py` to load `.gguf` llama.cpp or Latency Machine Learning Models. The `__init__` constructor built in the `Llama` takes several parameters to configure the loading and running of the model. Other than `NUMA, LoRa settings`, `loading tokenizers,` and `hardware settings`, `__init__` also loads the `chat template` from targeted `.gguf` 's Metadata and furtherly parses it to `llama_chat_format.Jinja2ChatFormatter.to_chat_handler()` to construct the `self.chat_handler` for this model. Nevertheless, `Jinja2ChatFormatter` parse the `chat template` within the Metadate with sandbox-less `jinja2.Environment`, which is furthermore rendered in `__call__` to construct the `prompt` of interaction. This allows `jinja2` Server Side Template Injection which leads to RCE by a carefully constructed payload.

## Source-to-Sink

### `llama.py` -> `class Llama` -> `__init__`:

```python class Llama: """High-level Python wrapper for a llama.cpp model."""

__backend_initialized = False

def __init__( self, model_path: str, # lots of params; Ignoring ): self.verbose = verbose

set_verbose(verbose)

if not Llama.__backend_initialized: with suppress_stdout_stderr(disable=verbose): llama_cpp.llama_backend_init() Llama.__backend_initialized = True

# Ignoring lines of unrelated codes.....

try: self.metadata = self…