Author: Denis Avetisyan
Researchers have developed a novel file format that enhances the security and privacy of large language models during distribution and use.
CryptoTensors extends the Safetensors format with tensor-level encryption and access control policies for confidential model sharing.
While large language models increasingly rely on sensitive data for customization, existing model formats lack robust, built-in security features for confidential distribution and deployment. This paper introduces CryptoTensors, a light-weight large language model file format-built as an extension to Safetensors-designed to address this critical gap. By incorporating tensor-level encryption and embedded access control, CryptoTensors enables secure model sharing with minimal performance overhead. Could this approach become a standard for safeguarding intellectual property and user privacy in the rapidly evolving landscape of LLM applications?
Architecting Trust: The Expanding Landscape of Large Language Models
Large Language Models (LLMs) are swiftly establishing themselves as core components across a widening spectrum of artificial intelligence applications, from sophisticated chatbots and content creation tools to code generation and data analysis platforms. However, this rapid integration isn’t without its challenges; concerns regarding security and access control frequently impede widespread deployment. The very power that makes LLMs so versatile – their ability to generate human-quality text and adapt to diverse prompts – also presents risks, including the potential for malicious use, data breaches, and the propagation of misinformation. Consequently, organizations are increasingly focused on implementing robust safeguards, such as encryption, access restrictions, and continuous monitoring, to mitigate these vulnerabilities and ensure responsible innovation in this transformative field. The balance between unlocking the full potential of LLMs and maintaining a secure, trustworthy AI ecosystem remains a critical focus for developers and policymakers alike.
Large language models are increasingly diverse in their accessibility and design, broadly categorized as either closed-weight or open-weight. Closed-weight models, exemplified by systems like Claude and Gemini, maintain their core parameters as proprietary intellectual property, typically offered through application programming interfaces (APIs) or managed services. Conversely, open-weight models – including LLaMA, Mistral, and Gemma – publicly release their model weights, enabling researchers and developers to download, customize, and deploy them independently. This distinction fosters a unique ecosystem; closed-weight models prioritize ease of use and centralized control, while open-weight models empower innovation and community-driven development, albeit requiring more technical expertise and infrastructure for implementation. The choice between these approaches hinges on balancing factors such as cost, control, customization needs, and security considerations.
The burgeoning accessibility of open-weight large language models presents a unique set of security challenges. Unlike their closed-weight counterparts, these models allow for extensive customization and deployment on diverse infrastructure, fostering innovation but simultaneously expanding the potential attack surface. Robust security measures are therefore paramount; without them, these models become vulnerable to unauthorized access, data breaches, and malicious manipulation – including the injection of biased or harmful outputs. Effective safeguards necessitate a multi-layered approach, encompassing stringent access controls, continuous monitoring for anomalous activity, and techniques for verifying the integrity of the model itself. Protecting these models isn’t merely about preserving intellectual property, but about ensuring the trustworthiness and responsible application of increasingly powerful AI technologies.
A Secure Foundation: Introducing CryptoTensors for Model Distribution
CryptoTensors is a new file format for large language model (LLM) distribution, built as an extension of the Safetensors format. Existing LLM distribution methods often lack robust security features, leaving models vulnerable to tampering and unauthorized access. CryptoTensors directly addresses these vulnerabilities by incorporating cryptographic security measures without significantly increasing file size or impacting performance. Benchmarking results indicate minimal overhead associated with the encryption and signature processes. The format is designed to facilitate secure storage and transmission of model weights, ensuring data confidentiality and integrity throughout the model lifecycle.
CryptoTensors utilizes AES-GCM-256, a symmetric encryption algorithm, to provide confidentiality of model weights during both transmission and storage. This algorithm employs a 256-bit key and Galois/Counter Mode to ensure data secrecy and authenticity. In addition to encryption, each CryptoTensor file is cryptographically signed using the ED25519 digital signature scheme. ED25519 provides strong authentication, verifying the model’s origin and ensuring data integrity; any modification to the model will invalidate the signature. This dual approach of encryption and digital signatures mitigates risks associated with unauthorized access and malicious tampering of large language models.
The Key Broker Service is a central component of the CryptoTensors system, responsible for the secure generation, storage, and distribution of Data Encryption Keys (DEKs). These DEKs are used to encrypt the model weights serialized in the CryptoTensors format. Access to DEKs is governed by a fine-grained access control policy, allowing model owners to specify which users or services are authorized to decrypt specific models. The service employs robust key management practices, including key rotation and secure storage, to mitigate the risk of unauthorized decryption. By decoupling key management from the model files themselves, the Key Broker Service prevents scenarios where compromised model files directly expose decryption keys, thereby enhancing overall security.
CryptoTensors incorporates lazy loading into standard model serialization processes to significantly reduce initial memory requirements. Rather than loading the entire model into memory at once, CryptoTensors allows for loading of individual tensors on demand as they are needed for computation. This approach is particularly beneficial for large language models (LLMs) where the complete model size can exceed available RAM. Implementation involves restructuring the serialized model file to store tensors in a compressed, fragmented format, with metadata indicating their location and dependencies. This minimizes the memory footprint during model loading and enables efficient processing of models that would otherwise be impractical to deploy on resource-constrained devices.
Granular Control: Policy-Driven Access Control and Advanced Security Features
CryptoTensors leverages Open Policy Agent (OPA) for fine-grained access control, employing Rego, OPA’s declarative policy language, to define authorization rules. This integration enables administrators to specify access permissions based on a variety of factors, including user roles, model attributes, and contextual information. Rego policies are evaluated against incoming requests to determine whether access should be granted, effectively controlling who can view, modify, or deploy specific model weights within the system. The declarative nature of Rego allows policies to be defined independently of the application code, promoting separation of concerns and simplifying policy management and auditing.
CryptoTensors enables administrators to define access control policies for model weights based on a combination of role-based access control (RBAC), attribute-based access control (ABAC), and contextual factors. Specifically, policies can be configured to grant or deny access to model weights based on the user’s assigned role within the organization (e.g., data scientist, engineer, auditor), user or model attributes (e.g., department, security clearance level, model sensitivity), and runtime context (e.g., time of day, source IP address, deployment environment). This granular control extends to all operations involving model weights, including reading, writing, and deployment, ensuring that only authorized personnel can perform sensitive actions.
Performance evaluations indicate that encrypting models introduces overhead during serialization and deserialization. Specifically, model serialization times increased by 61.3% when using NumPy and 62.0% with PyTorch following encryption. Deserialization experienced substantially higher overhead, with NumPy showing a 125.5% increase and PyTorch a 5808.9% increase in processing time. These figures represent the additional time required for encryption/decryption processes during the saving and loading of model weights, respectively, and are based on empirical measurements conducted during testing.
Performance evaluations with vLLM demonstrated an 8x increase in model loading time when utilizing encrypted models. Despite this increase in loading time, inference throughput remained stable, exhibiting only a ±3% change. Furthermore, first-token latency was minimally impacted, varying by ±7%. These results indicate that while encryption introduces a loading time overhead, it does not significantly degrade real-time inference performance, suggesting negligible impact on operational efficiency during model serving.
CryptoTensors extends security measures beyond standard access controls through the implementation of Model Watermarking, a technique to embed identifying information within model weights to deter unauthorized use or distribution. Simultaneously, the platform investigates advanced cryptographic techniques, specifically Homomorphic Encryption (HE) and Secure Multi-Party Computation (MPC). HE allows computations to be performed on encrypted data without decryption, preserving data confidentiality throughout the process. MPC enables multiple parties to jointly compute a function over their private inputs while ensuring that no individual party learns the others’ data. These explorations aim to provide additional layers of protection for sensitive model data and facilitate secure collaboration without compromising privacy.
CryptoTensors is designed for compatibility with widely adopted machine learning tools to simplify integration into existing workflows. Specifically, it offers native support for Hugging Face Transformers, allowing users to directly load and utilize models trained and stored within the Hugging Face ecosystem. Furthermore, CryptoTensors integrates with efficient inference engines such as vLLM, enabling optimized model serving with minimal performance impact; testing indicates an 8x increase in model loading time with encrypted models, but stable inference throughput with ±3% change and minimal impact on first-token latency at ±7%. This framework compatibility reduces the barrier to adoption by leveraging existing infrastructure and expertise.
A Collaborative Future: The Impact of Secure and Open AI Ecosystems
The proliferation of large language models (LLMs) is hampered by concerns surrounding intellectual property, security, and responsible use; CryptoTensors addresses these challenges by establishing a framework for secure and auditable distribution. This system leverages cryptographic techniques to ensure the integrity and provenance of LLMs, allowing developers to confidently share and collaborate on these powerful tools. By providing a verifiable record of model lineage and access, CryptoTensors mitigates risks associated with unauthorized modification or malicious use, fostering a more trustworthy environment for innovation. The ability to demonstrably track an LLM’s journey – from creation to deployment – encourages broader participation and accelerates the development of beneficial AI applications, ultimately building a collaborative ecosystem where trust is foundational.
A truly open and accessible artificial intelligence ecosystem hinges on the ability of researchers and developers to readily build upon existing work, and new tools are poised to dramatically lower the barriers to entry. By simplifying the process of sharing and integrating large language models, these advancements enable a wider range of individuals and institutions to participate in AI innovation. This democratization fosters a more vibrant and diverse landscape, accelerating the development of novel applications in fields ranging from personalized medicine to sustainable agriculture. The resulting surge in creativity and collaboration promises not only to refine existing AI technologies, but also to unlock unforeseen possibilities and address complex challenges with greater efficacy and inclusivity.
A resilient framework for responsible AI governance is established through the convergence of robust encryption, nuanced policy-driven access controls, and seamless integration with pre-existing development tools. This system doesn’t simply secure large language models; it allows for granular permissioning, defining exactly who can access, modify, or redistribute AI assets. Strong encryption safeguards the models themselves, while the policy layer ensures compliance with evolving ethical guidelines and legal requirements. Critically, compatibility with current workflows minimizes disruption and accelerates adoption, fostering an environment where innovation and responsible development are not mutually exclusive. This integrated approach moves beyond reactive security measures, enabling proactive and auditable control over the entire AI lifecycle, ultimately building trust and accountability within the rapidly expanding AI landscape.
CryptoTensors significantly advances the field of privacy-preserving artificial intelligence by creating a secure environment for sensitive data collaboration. Traditional machine learning often requires centralizing data, raising substantial privacy concerns; however, CryptoTensors facilitates federated learning, allowing models to be trained across decentralized datasets without exchanging the data itself. This is further enhanced by the potential for integrating differential privacy techniques, adding carefully calibrated noise to the learning process to obscure individual contributions while maintaining overall data utility. The system’s cryptographic foundation extends beyond these core concepts, opening avenues for secure multi-party computation and homomorphic encryption, ultimately enabling complex AI tasks to be performed on encrypted data – a paradigm shift with profound implications for industries handling confidential information, such as healthcare and finance, and fostering trust in increasingly data-driven applications.
The pursuit of secure large language model distribution, as detailed in this work, echoes a fundamental principle of system design: structure dictates behavior. CryptoTensors, by embedding access control and encryption directly within the file format itself, moves beyond simply protecting the model as a whole and focuses on securing individual tensors. This granular approach to security, building control into the foundational structure, aligns perfectly with the idea that a system’s integrity is determined not by isolated defenses, but by the inherent properties of its design. As Henri Poincaré observed, “It is through science that we arrive at truth, but it is through simplicity that we arrive at clarity.” CryptoTensors strives for that clarity by providing a straightforward, structurally sound method for confidential model distribution.
Future Architectures
The introduction of CryptoTensors addresses a critical, if often belatedly acknowledged, vulnerability in the rapid proliferation of large language models. One notes a certain historical pattern: infrastructure bolted on after the city is built. This work represents a shift – attempting to embed security not as an afterthought, but as a fundamental aspect of the model’s very structure. However, tensor-level encryption is merely one layer. True control demands a more holistic consideration of the entire computational graph, extending beyond static file formats to encompass runtime environments and access policies that dynamically adapt to evolving threats.
The current approach, while promising, highlights the inherent tension between security and usability. Complex key management, as always, threatens to become a bottleneck. Future iterations must focus on streamlining these processes, potentially leveraging emerging paradigms like homomorphic encryption or secure enclaves to minimize performance overhead. One imagines a future where model distribution resembles a carefully managed utility, rather than a free-for-all download – but achieving that requires elegant solutions, not simply more complexity.
Ultimately, the real challenge lies not in securing the model itself, but in securing the interaction with the model. Differential privacy, federated learning, and verifiable computation will likely become integral components of a robust security framework. The goal is not simply to prevent unauthorized access, but to ensure that even authorized usage respects the privacy and security of all stakeholders. A system that treats security as a modular add-on is destined to crumble; it must be woven into the very fabric of its design.
Original article: https://arxiv.org/pdf/2512.04580.pdf
Contact the author: https://www.linkedin.com/in/avetisyan/
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2025-12-07 16:17