BlindLlama

BlindLlama is an open-source, zero-trust AI inference platform developed by Mithril Security that enables developers to query open-source large language models (LLMs) without exposing their data to the AI provider or any intermediary. It is designed for ML engineers, security teams, and privacy-conscious organizations that need to use managed AI models while maintaining strict data confidentiality and verifiable privacy guarantees. Unlike conventional AI APIs that require trusting the provider with sensitive prompts, BlindLlama ensures that even the provider's administrators cannot see the data. The project builds on Mithril Security's earlier work with BlindAI (a confidential inference solution audited by Quarkslab) and BlindChat (a privacy-first conversational AI), and is currently under active development with a client-side Python SDK and server components available on GitHub, where the client repository has 40 stars and 4 forks as of mid-2025.

BlindLlama works by deploying a hardened AI container within a Trusted Execution Environment (TEE) that uses Trusted Platform Modules (TPMs) to generate cryptographic proofs of code integrity and environment authenticity. An attestation station server loads the hardened container, and the client-side Python SDK verifies these proofs before sending any data, ensuring the client is communicating with a genuine, untampered environment. The system blocks all potential points of data exposure, including SSH access, server logs, network monitoring, and administrative interfaces, providing end-to-end protection. This architecture delivers faster inference performance compared to Mithril's earlier BlindAI solution, as it focuses on serving managed models directly to developers rather than requiring self-hosted model deployment, making it suitable for production workflows where speed and privacy are both critical.

In the competitive landscape of AI security and privacy tools, BlindLlama positions itself as a free and open-source alternative to commercial offerings such as Lakera, Meta's Llama Guard, Promptfoo, Lasso Security, Braintrust, Protect AI, and Securiti. Unlike many competitors that focus on content filtering, prompt injection detection, or access control, BlindLlama's core differentiator is its hardware-backed cryptographic verification of privacy controls, providing a zero-trust architecture that does not rely on the AI provider's goodwill. However, it is still under development and does not yet offer full security features, and it depends on cloud providers and hardware vendors for some level of trust in the underlying TEE infrastructure, which may not satisfy the most stringent threat models.

The honest trade-offs with BlindLlama include its reliance on cloud and hardware providers for the integrity of the TEE, meaning that a compromised hypervisor or CPU vulnerability could theoretically undermine its guarantees. The project is also in an early stage, with the server-side repository (blindllama-v2) showing only 16 stars and limited recent activity as of March 2025, indicating a small community and potentially slower iteration on security audits and feature completeness. Additionally, because BlindLlama is designed specifically for managed open-source LLMs, it does not support proprietary models or custom model deployment, which may limit its applicability for teams needing to run their own fine-tuned or closed-source models. Finally, the requirement for TPM-compatible hardware and attestation infrastructure adds operational complexity compared to simpler API-based solutions, and the lack of a managed cloud service means users must set up and maintain their own attestation servers.

Free and open-source; no paid tiers or usage fees are currently offered.