Technology Firms Face Risks As AI Models Expose Sensitive Data

Anes Abdennebi and colleagues at the Software and IT Engineering Department, have integrated post-quantum cryptography, specifically lattice-based homomorphic encryption, directly into the inference pipeline of the LLAMA-3 model. The method addresses vulnerabilities to both conventional and potential quantum computing attacks, offering a pathway to secure LLM operations without fully sacrificing performance. Results indicate high text generation accuracies, reaching up to 98%, with reasonable latencies of 237ms and a throughput of up to 80 tokens per second, suggesting the practical feasibility of privacy-preserving LLM inference.

Real-time LLM inference secured with post-quantum cryptography and homomorphic encryption

A significant advancement in LLM security has achieved 80 tokens per second, overcoming previous limitations that restricted fully homomorphic encryption (FHE) to markedly slower processing speeds. Historically, the computational intensity of FHE rendered its application to real-time LLM inference impractical. Traditional encryption methods, while effective against classical attacks, are increasingly vulnerable to the anticipated capabilities of quantum computers, necessitating a shift towards post-quantum cryptography. Before this development, securing LLAMA-3’s inference pipeline with Post-Quantum Cryptography and Lattice-based Homomorphic Encryption was impractical due to unacceptable latency. However, the integration of these techniques now yields up to 98% text generation accuracy. This performance, alongside a latency of 237ms on an i9 CPU, confirms the feasibility of privacy-preserving LLM inference for real-world applications, particularly in sectors handling sensitive data. The ability to process data while encrypted is crucial for maintaining confidentiality throughout the entire inference process, from input to output.

Homomorphic encryption allows computation on encrypted data without decryption, safeguarding sensitive information against both conventional and potential quantum computing attacks. Lattice-based cryptography, a specific type of post-quantum cryptography, relies on the mathematical hardness of solving problems on lattices, a complex, high-dimensional structure. This approach is considered resistant to known quantum algorithms, such as Shor’s algorithm, which poses a threat to widely used public-key cryptosystems like RSA and ECC. Securing the LLAMA-3 model’s inference pipeline with Post-Quantum Cryptography and Lattice-based Homomorphic Encryption achieves up to 98% text generation accuracy, a method enabling computation on encrypted data. Validated using an i9 CPU, the system delivered a latency of 237ms and sustained a throughput of 80 tokens per second, representing a substantial improvement over prior limitations. Further analysis revealed successful data processing without decryption, mitigating risks from current and future quantum computing threats. The team specifically targeted layers within the transformer architecture to optimise performance, and confirmed the framework is compatible with the concrete-ml library. The concrete-ml library provides pre-built components and optimisations for implementing FHE schemes, simplifying the integration process and enhancing performance. Optimising the transformer layers, which are central to LLM functionality, was critical to minimising the performance overhead introduced by the encryption process.

LLAMA-3 inference secured against quantum decryption using post-quantum cryptography

Protecting sensitive data within large language models is now vital as these systems permeate sectors from finance to healthcare. The increasing deployment of LLMs in these domains necessitates robust security measures to protect confidential information from unauthorised access and potential breaches. While this development successfully integrates post-quantum cryptography into LLAMA-3, shielding it from both current and potential quantum attacks, it acknowledges a key limitation. The current work focuses exclusively on a single model and a specific CPU configuration, and does not represent universal compatibility across all large language models or hardware configurations. Further research is needed to assess the generalisability of this approach to other LLM architectures, such as those based on different transformer designs or utilising alternative activation functions. Moreover, performance characteristics may vary significantly depending on the underlying hardware, including GPU models and memory configurations.

Demonstrating post-quantum cryptographic protection within an LLM’s inference pipeline, the process of generating text, establishes a strong proof of concept. This achievement validates the feasibility of safeguarding sensitive data against evolving quantum threats, even with current technology and reasonable processing speeds of eighty tokens per second. Successfully integrating post-quantum cryptography into a large language model safeguards data against future quantum computing threats. The implications extend beyond simply protecting data at rest; it enables secure computation on data in transit and during processing, providing a comprehensive security solution. This is particularly important in scenarios where data is shared between multiple parties or processed in untrusted environments.

Utilising LLAMA-3 and homomorphic encryption, this demonstration proves data can be protected during text generation with acceptable speeds. A functional system securing large language models with post-quantum cryptography is established, a key step given the changing threat field. By integrating lattice-based homomorphic encryption, a technique allowing computation on encrypted data without revealing it, into the LLAMA-3 model’s inference pipeline, the team addressed vulnerabilities to both conventional and future quantum computing attacks. Achieving 80 tokens per second demonstrates practical feasibility, validating the concept of privacy-preserving LLM inference without severely impacting performance. This advance moves beyond theoretical security, offering a proactive defence for sensitive data processed by increasingly prevalent language models. The ability to maintain a throughput of 80 tokens per second is crucial for enabling interactive applications and real-time responses, making the system suitable for a wide range of use cases. Future work could explore techniques for further optimising performance, such as model compression or hardware acceleration, to achieve even higher throughput and lower latency. This research represents a crucial step towards building more secure and privacy-preserving LLM systems, paving the way for wider adoption in sensitive domains.

The researchers successfully integrated post-quantum cryptography into the LLAMA-3 large language model, securing its layers against data privacy attacks. This is important because it proactively defends sensitive data from both current and potential future threats posed by quantum computing. By utilising lattice-based homomorphic encryption, the team demonstrated secure computation on encrypted data without significantly impacting performance, maintaining a throughput of eighty tokens per second. The authors suggest future work may focus on optimising performance through techniques like model compression or hardware acceleration.