Mithril Security
Mithril Security provides a hardware-enforced privacy layer for LLMs, ensuring that neither the infrastructure provider nor the AI vendor can access sensitive prompt data during inference.
Best for regulated industries like healthcare and finance that need to use open-source LLMs without violating data residency or privacy mandates.
Analysis based on product data, pricing structure, traffic signals, and public user sentiment.
Who Should Use Mithril Security?
Typical users
Security engineers, CTOs in highly regulated sectors, and AI developers handling PII or proprietary IP.
Maturity fit
advanced
Choose this if…
- You need to process highly sensitive data (PII, health records) through LLMs.
- Your compliance team forbids sending data to third-party AI providers like OpenAI.
- You require cryptographic proof (attestation) that your data is processed in a secure enclave.
- You want to deploy open-source models like Llama 3 or Mistral with zero-trust architecture.
Skip this if…
- You are building low-risk internal tools where standard VPC isolation is sufficient.
- You require the absolute lowest latency possible, as secure enclaves introduce a performance tax.
- You only use closed-source models like GPT-4, which cannot be audited in the same way.
About Mithril Security
Mithril Security is a specialized infrastructure provider focused on Confidential AI. It enables organizations to deploy and query large language models within Trusted Execution Environments (TEEs), ensuring data remains encrypted even while being processed.
Official profiles
What it actually does
The platform wraps AI models in secure hardware enclaves (like Intel SGX or NVIDIA TEEs) to provide 'Blind Inference.' This allows users to send data to a model where the host cannot see the input, the output, or the model weights, backed by cryptographic attestation.
What makes it different
Unlike standard cloud security that protects data at rest or in transit, Mithril focuses on 'encryption-in-use.' They provide a bridge between complex hardware-level security (Confidential Computing) and high-level AI development, making it possible to verify the integrity of the remote environment via open-source tooling.
Key Features
BlindLlama
A private API that allows querying Llama models inside a secure enclave without data exposure.
Remote Attestation
Provides a cryptographic certificate proving the exact code and hardware running the model.
BlindBox
A tool to containerize and deploy any AI model into a secure enclave with minimal code changes.
AICert
An open-source framework to create traceable and verifiable AI model deployments.
Hardware-level Isolation
Uses Intel SGX and AMD SEV to prevent root users or OS-level attackers from memory scraping.
Hugging Face Integration
Directly deploy models from the Hugging Face hub into a hardened environment.
Pricing
BlindLlama (Beta/SaaS)
- Access to Llama 3 in TEEs
- Standard attestation
- Community support
Enterprise
- On-premise deployment
- Custom model support
- Dedicated H100/A100 enclaves
- SLA guarantees
- Priority support
Pricing checked 4 months ago
Pricing guidance
- When you need to deploy a custom or fine-tuned model.
- When you require dedicated GPU resources rather than shared API access.
- When your compliance department requires an on-premise or VPC-locked installation.
- Limited availability of TEE-enabled GPUs in certain cloud regions.
- Maximum context window limits may be lower due to enclave memory constraints.
Premium security positioning with pricing that reflects the high cost of specialized hardware and compliance value.
Pros & Cons
Strengths
-
Mathematical privacy guarantees
Uses hardware-based Trusted Execution Environments (TEEs) to ensure data is never visible to the infrastructure owner.
-
Simplified compliance
Provides the technical controls necessary to meet strict GDPR, HIPAA, and SOC2 requirements for AI data processing.
-
Open-source transparency
Much of the stack is open-source, allowing security teams to audit how the enclaves are managed and attested.
-
Model IP protection
Allows model owners to deploy their weights on third-party hardware without the host being able to steal the model.
Weaknesses
-
Performance overhead
Running models inside enclaves typically results in a 10-30% performance hit compared to bare-metal or standard GPU instances.
Affects: Latency-sensitive applications
-
Hardware lock-in
Requires specific cloud instances (e.g., Azure DC-series or AWS Nitro) which are more expensive and less available than standard instances.
Affects: Infrastructure teams
-
Complex debugging
The 'black box' nature of enclaves makes traditional logging and debugging significantly more difficult for developers.
Affects: DevOps and Backend Engineers
Real User Sentiment
Users view Mithril as a highly technical, 'hard-core' security solution that solves the trust gap in cloud AI.
Users tend to like
- The ability to verify the environment via attestation.
- Strong alignment with open-source AI communities.
- Clear documentation on the underlying hardware security.
Users commonly complain about
- High barrier to entry for developers unfamiliar with TEEs.
- Limited support for certain specialized GPU architectures.
- Latency issues in early-stage beta products.
Recurring tradeoffs
- You trade raw inference speed and cost for absolute data privacy and auditability.
Happiest users
Security architects at mid-to-large enterprises who need to 'greenlight' AI projects for sensitive departments.
Often frustrated
Developers looking for a quick, cheap API wrapper who don't actually need hardware-level security.
Use Cases
Medical Diagnosis
Analyzing patient records with Llama 3 while ensuring the data is never decrypted outside the enclave.
Financial Analysis
Processing proprietary trade data or customer PII through an LLM without cloud provider access.
Legal Document Review
Summarizing sensitive contracts where data residency and confidentiality are legally mandated.
Secure Model Hosting
A model creator selling access to their weights without allowing the hosting provider to copy the model.
Government/Defense
Running AI on classified or restricted datasets in a zero-trust cloud environment.
Frequently Asked Questions
How does Mithril Security compare to Azure Confidential Computing?
Azure provides the raw hardware (TEEs), while Mithril provides the software layer (BlindBox/BlindLlama) specifically optimized for AI. Mithril makes it easier to deploy models and verify attestation without writing low-level C++ or managing enclave memory manually.
Is Mithril Security open source?
Yes, many of their core components, including the AICert framework and the BlindLlama client, are open-source on GitHub. This is critical for their value proposition, as it allows users to verify that the 'blind' processing is actually happening as claimed.
What is 'Remote Attestation' and why does it matter?
Remote attestation is a cryptographic proof generated by the hardware (like an Intel CPU). It proves to the user that the model is running in a genuine secure enclave and that the code hasn't been tampered with. Without it, you are just taking the vendor's word for their security.
Does using Mithril slow down my AI?
Yes. Because data must be encrypted/decrypted at the hardware boundary and memory is isolated, there is a performance overhead. Expect a 10% to 30% increase in latency depending on the model size and the specific hardware used.
Can I use Mithril with OpenAI's GPT-4?
No. Mithril requires access to the model weights to load them into a secure enclave. Since GPT-4 is closed-source and hosted by OpenAI, you cannot wrap it in a Mithril enclave. This tool is designed for open-weights models like Llama, Mistral, and Falcon.
What cloud providers are supported?
Mithril can be used on any cloud provider that offers TEE-enabled hardware, including Microsoft Azure (DC-series), AWS (Nitro Enclaves), and GCP (Confidential VMs). They also support on-premise servers with compatible Intel or AMD CPUs.
Why trust this page?
This evaluation combines product positioning, pricing analysis, traffic and market signals, and public user sentiment into a single decision-support page. Content is generated editorially — not copied from the vendor's website.
Funding & Company
Founded
2021
Stage
Acquired
Total Raised
$1.3M
Latest Round
Pre-Seed (May 2023)
Notable Investors
Mithril Security raised a single pre-seed round of approximately $1.3 million (€1.2M) in May 2023 to develop its open-source confidential computing tools for AI. The company was then acquired by the secretive French AI startup 'H Company' in the latter half of 2024, shifting its stability and future prospects to its new parent.
Market Signals & Traffic
Estimated visits, global rank, geography, traffic sources, monthly visit trends, and organic search keywords (Similarweb)—on a dedicated page built for depth and search.
- Estimated visits
- 0
- Global rank
- —
- Snapshot
- May 2026
- Traffic trend
- —
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