In the constantly evolving realm of machine learning, serialization plays a pivotal role. It's the process by which we transform complex data structures and objects into a format that's easily stored and later reconstructed. Think of it as putting together a puzzle – breaking it down into individual pieces and then reassembling it.
At the heart of this procedure in Python-based ML platforms like PyTorch is a module innocuously
pickle. While it's innocently and playfully named, this method of
serialization is, despite its popularity, marred with potential hazards.
Pickle, as charming as its name might sound, carries with it an array of security
vulnerabilities, presenting risks that many in the community might be unaware of. The question
arises: Why would cutting-edge technologies risk using a tool that could become their Achilles heel?
The aim of this post is to dive into the why, shine a light on the dangers posed by
continuing down this road, and explore safer avenues that the community can, and arguably should,
Why Pickling is Problematic
To understand the intricacies of why
pickle is potentially perilous, we have to start
with its core functionality. At its heart,
pickle provides a way to serialize and
deserialize Python object structures. While this utility has undeniable advantages — especially in
terms of ease of use and efficiency — it comes with a significant catch: the module does not
inherently ensure the authenticity or integrity of the serialized data.
In other words, when you unpickle data, especially from unverified sources, you're essentially trusting that the serialized code you're executing hasn't been tampered with or manipulated.
The gravest security risk posed by the
pickle module is its capability to
execute arbitrary code during unpickling. An attacker can craft a malicious payload that,
when unpickled, could run harmful code on the victim's machine. This can lead to malicious
activities, from data theft and unauthorized access to the launch of Distributed Denial of Service
(DDoS) attacks, and also means every time you load weights you're exposing yourself to Remote
Code Execution vectors. Unfortunately, the flexibility and power of
pickle — which is
part of what makes it appealing — is also its greatest vulnerability.
Consider a hypothetical scenario: a machine learning researcher, working on a groundbreaking project, receives a serialized model via email. Eager to integrate this model into their work, the researcher unpickles the model without a second thought. Unknown to the researcher, they're being phished, and the attacker modified the serialized object to include a malicious payload. The moment that model is unpickled, the attacker's code executes, potentially granting them unauthorized access to sensitive project details or even the entire research network.
This gets so much worse when we start considering that the rise of LLMs has made AI a hot topic in the government and in the DoD. What if, in the above scenario, the researcher works for a government contractor?
Or maybe malicious weights snuck through in an open source project. It's pretty common for folks to volunteer to train a large project on their GPU cluster. What would stop e.g. China or Russia from maliciously injecting some code into the weights that are unlikely to be checked thoroughly?
Once unpickled on a secure network, it could poison the host and rapidly spread on trusted domains.
It's crazy to think the
pickle module poses a risk to our national security, but it
also poses by far the easiest attack vector, and one unlikely to be caught by antivirus at
PyTorch and the Pickle Predicament
Now, bringing this back to the context of PyTorch: like many machine learning frameworks, PyTorch
pickle for serialization and deserialization. When a user saves a PyTorch
model, the weights, architecture, and other components get pickled. When that model is later loaded
for further training or inference, it's unpickled. This cycle, while efficient, exposes users to
the security vulnerabilities associated with
Given PyTorch's stature in the machine learning community, the potential fallout from such vulnerabilities is limitless. PyTorch is, by faaar, the most popular ML framework and the one most often used with Transformers or language modeling. With countless researchers and institutions relying on PyTorch for their projects, a single maliciously crafted model or tensor could wreak havoc on a potentially international scale.
Furthermore, the machine learning community thrives on collaboration. Pre-trained models, weights,
and architectures are frequently shared across researchers, teams, and even continents. This global,
interconnected ecosystem, while fostering rapid innovation, also amplifies the potential damage that
can stem from the misuse of tools like
pickle offers a seamless way to serialize and deserialize data in Python, its
underlying security flaws, especially in the context of platforms like PyTorch, cannot be
ignored any longer. As the machine learning community continues to grow and evolve,
it's crucial that we re-evaluate the tools we depend upon and seek out more secure alternatives.
AI in Secure Contexts: A Brewing Storm
Malware Threat in Models**
LLMs, with their vast potential and plethora (yes, I used that word) of applications, promises to revolutionize multiple sectors, including those demanding high levels of security such as defense, healthcare, and finance. But if we integrate AI into these high risk, highly secure environments, we need to be hyper-vigilant about malware beign embedded in these models.
The intricacy of PyTorch models, combined with the variation in model architecture, makes them an attractive vehicle for malicious actors. A model, on the surface, may appear benign, performing its intended function without any apparent issue. But lurking within its layers could be hidden malicious code or behaviors, designed to activate under specific conditions or to subtly manipulate outputs.
For example, consider a deep learning model integrated into a defense surveillance system. If this model has been tampered with, it might be designed to occasionally overlook specific patterns or objects, creating potential security breaches. Alternatively, an AI-driven health diagnostic tool might be manipulated to give false negatives or positives, risking lives in the process.
This would be virtually undetectable. In production, these systems process hundreds of thousands of samples a day, and it would be difficult to get a statistical analysis to highlight potential security issues unless you were specifically looking for them.
For even more widespread impact, write a research paper! Put out a paper claiming stellar results on your specific embedding technique. As is now common and somehow widely accepted, put out no code for your model training, just the pickled weights for the embedding and a pretty table claiming you can beat GPT-4 on HumanEval. With the right marketing you could be trending on HuggingFace within hours, and in a few days someone will have rushed to get your embedding into production to get an edge on their competitors. By the time someone will have actually executed HumanEval on your embedding model or replicated your paper, your malware will have spread across thousands of organizations and secure contexts.
This threat isn't just theoretical. There have been growing concerns and instances where AI models, especially those of unknown or dubious origin, were found to have concealed behaviors or embedded backdoors. The reality is that any AI model, when compromised, can become a Trojan Horse, providing malicious actors with unprecedented access or influence.
The Risk of Private Model Training
The current AI landscape often sees organizations resorting to a practice that further compounds this risk: keeping the model training code private and only releasing the trained weights to the public. On the surface, this might seem like a reasonable trade-off. Companies and researchers might want to protect proprietary methods, data, or intellectual property. But this practice can have unintended security consequences.
By just sharing the model's weights and not its training details or methodology, there's an absence of transparency. Without insight into how a model was trained, its data sources, or the exact processes involved, how can one truly vouch for its integrity? It's akin to being handed a sealed black box, and while the outputs might seem correct, there's no telling what's happening inside.
Moreover, an attacker could exploit this practice by releasing compromised models with hidden malicious behaviors. Since the community doesn't have access to the training code or methodology, detecting such tampering becomes significantly harder.
Accepting this practice as commonplace could underline all future AI development with heavy security risks.
In sectors where security is paramount, like government or defense, the stakes are even higher. The integration of a compromised AI model, especially one with concealed malicious intent, could have catastrophic consequences. It could lead to misinformation campaigns, security breaches, or even the undermining of essential services.
Given the increasing reliance on AI and the high stakes involved, the call for transparency and rigorous security checks has never been louder. While the allure of AI promises efficiency, accuracy, and scalability, it must not come at the cost of security and trustworthiness. Adopting robust security practices and fostering a culture of transparency can ensure that as we ride the AI wave, we don't get engulfed by the brewing storm.
Exploring Safer Alternatives
In light of these risks associated with
pickle, we think the broader community needs
to come together and advocate for safer alternatives. Many organizations have already been proactive
in its pursuit of more secure, efficient, and transparent solutions.
ONNX (what a cool sounding name)
One of the most notable alternatives to the traditional pickling method is the Open Neural Network Exchange (ONNX). Designed to provide an interoperable format for machine learning models, ONNX aims to alleviate some of the concerns tied to model serialization and sharing.
At its core, ONNX offers a platform-neutral way to represent machine learning models. What this means is that models trained in one framework (like TensorFlow or PyTorch) can be converted to ONNX format and subsequently imported into another framework for further refinement or deployment. This cross-compatibility breaks down silos, fostering greater collaboration.
But where ONNX truly shines is its transparency. Given that it's an open standard, models represented in ONNX format can be thoroughly inspected and vetted, reducing the risks of hidden malware or malicious behaviors. Furthermore, as ONNX grows in popularity and adoption, there's a collective drive to ensure it meets stringent security benchmarks, making it a more trustworthy alternative for model serialization and sharing.
Counterfit (yes, it's Microsoft)
As AI continues to permeate various sectors, there's a growing need for specialized tools to assess and ensure the security of AI models. Microsoft's Counterfit is one such tool, designed from the ground up to provide a comprehensive security assessment for AI models.
Counterfit allows users to systematically evaluate AI models for potential vulnerabilities, ensuring that they're robust against adversarial attacks. By simulating a wide range of adversarial inputs and testing the model's reactions, Counterfit provides invaluable insights into potential weak spots or exploitable behaviors. This kind of rigorous testing can highlight vulnerabilities before a model is deployed in a real-world, high-stakes scenario.
Moreover, Microsoft's commitment to Counterfit, combined with its open-source nature, means that the tool is continuously updated to address emerging threats. For organizations looking to ensure the integrity and security of their AI models, Counterfit represents a robust line of defense.
Other Noteworthy Alternatives
While ONNX and Counterfit are at the forefront of this shift to security, several other noteworthy alternatives are also gaining traction:
JSON Serialization: Unlike the binary format of
pickle, JSON offers a human-readable format for serialization. Its simplicity and widespread adoption make it a safer choice for many serialization tasks, especially when the primary concern is data integrity and transparency. Of course, without compression, this is also the least efficient way to store weights.
Protocol Buffers (Protobuf): Developed by Google, Protocol Buffers offer a method for serializing structured data. Not only are they more efficient than
pickle, but they also carry fewer security risks, especially when dealing with data from untrusted sources. This is also the model serializing technique utilized by Tensorflow.
Apache Avro: If you're looking for enterprise solutions, there's always Apache Avro. Tailored for big data use cases, Apache Avro provides a framework for data serialization that is resilient to schema changes. Its focus on compatibility and robustness, coupled with its open-source nature, makes it a favorable choice for many data-intensive applications.
As the realm of AI continues to expand and evolve, the importance of security cannot be overstated. We believe the community is cognizant of the risks and is actively striving for a safer future. Adopting these safer alternatives not only ensures the security and trustworthiness of AI models but also bolsters the community's confidence in leveraging AI's vast potential.
Finally, a note for HuggingFace
It's impossible to overlook the impact of HuggingFace as a hub for model sharing and collaboration. With its expansive repository of pre-trained models and its commitment to fostering a collaborative AI community, HuggingFace is undeniably shaping the future of machine learning.
HuggingFace's model hub is akin to a vast library, tempting you with models contributed by researchers from around the globe. From cutting-edge NLP transformers to niche models tailored for specific tasks, they've democratized access to state-of-the-art AI. We're beyond grateful to them for everything they've done to accelerate open source models.
However, the very feature that makes HuggingFace invaluable - the sharing of pre-trained models - also introduces potential risks. As highlighted earlier, models can be tampered with, and malicious actors might exploit such platforms to spread compromised models. Thus, the role of platforms like HuggingFace becomes two-fold: to foster collaboration and to ensure the integrity and security of the shared models.
While HuggingFace does have mechanisms in place to vet and review models, the sheer volume and diversity of contributions make this a challenging endeavor. This is where the integration of external security assessment tools, like Microsoft's Counterfit, could play a pivotal role. By adopting such tools as a standard part of the model vetting process, platforms like HuggingFace can offer an added layer of assurance to their users.
Imagine a scenario where, alongside details like model architecture and performance metrics, there's a security score or badge, indicating the model's resilience against adversarial attacks or its compliance with certain security benchmarks. Such a feature wouldn't just protect end-users, it would also help users evaluate models for different use cases: e.g. for education, checking whether the model is resilient to prompt injection and general mischief.
HuggingFace, given its influence, is in a unique position to set industry standards. By championing security-centric practices, it can inspire other platforms and the broader AI community to prioritize safety and transparency. By taking the lead in integrating security assessment tools, advocating for model transparency, and promoting rigorous vetting processes, HuggingFace can set a paradigm that others can emulate.
In essence, as the nexus between AI innovation and collaboration, platforms like HuggingFace carry the torch for a future where AI is not just advanced, but also safe and trustworthy. We'd love to see HuggingFace add something similar to a security rating and hope this inspires some action.