Tinfoil Containers lets you deploy your application backend, your training pipeline, or your proprietary model on Tinfoil and build end-to-end verifiably private AI services.
Tinfoil - Private AI
AI that keeps your data private at all times. Fast, powerful, and verifiable, thanks to secure hardware enclaves.
Tinfoil Private Chat
Private AI chat application supporting open source models through Tinfoil
Backend infrastructure - Tinfoil Documentation
This page provides a description of the different components that make up our backend infrastructure. It also describes how Tinfoil guarantees code auditability and data confidentiality using these components.
How Tinfoil Proves Exactly What Model Is Running
How we cryptographically guarantee that we are serving specific, untampered model weights that clients can verify on each request.
AI proxy: fostering a more open ecosystem - Blog - Braintrust
Introducing Braintrust's latest feature: an AI proxy that lets you use open source models like LLaMa 2 and Mistral, as well as all of OpenAI's and Anthropic's models, behind a single interface with caching, security, and API key management built in.
Containers From Scratch • Liz Rice • GOTO 2018
Quantifying Frontier LLM Capabilities for Container Sandbox Escape
AI agents are demonstrating rapid improvement in capabilities: the length of some tasks that frontier models can complete autonomously—measured in human-equivalent time—has been doubling approximately every seven months (METR, 2025; AI Security Institute, 2025a). In cybersecurity, current models achieve non-trivial success (Zhang et al., 2025) on professional-level Capture the Flag challenges, and recent evaluations report 13% success rates on exploiting real-world web application vulnerabilities (Zhu et al., 2025b). These results indicate that modern models can already perform multi-step vulnerability discovery and exploitation.
The lethal trifecta for AI agents: private data, untrusted content, and external communication
If you are a user of LLM systems that use tools (you can call them “AI agents” if you like) it is critically important that you understand the risk of …
Titan in depth: Security in plaintext | Google Cloud Blog
While there are no absolutes in computer security, we design, build and operate Google Cloud Platform (GCP) with the goal to protect customers' code and data. We harden our architecture at multiple layers, with components that include Google-designed hardware, a Google-controlled firmware stack, Google-curated OS images, a Google-hardened hypervisor, as well as data center physical security and services.
A Strategy for Protecting Content from AI
Because that’s the world we live in these days.
Further Developments About Internal AI Models Hacking Things
If I had a nickel for every major leading AI lab that sheepishly admitted that the model it thought was sandboxed had, during a cybersecurity evaluation with its safeguards lowered, successfully hacked outside companies, I would have two nickels.
Coasts — Containerized Hosts for AI Agents
Free, open source parallel runtimes for AI agents. Run multiple isolated environments on your machine — no cloud, no conflicts.
Apple’s Containerization Framework and the Future of Secure Computing
The container revolution transformed how we deploy and manage applications, but it also inherited fundamental security and resource…
Why I hope Apple keeps investing in on-device AI
Edge intelligence is safer, more private, and less resource-intensive than cloud-based AI services.
HUGE — AI Without Giving Up Your Privacy
We're building an Agentic AI Platform running on your Device that lives with YOU, isolated from the cloud, fundamentally reimagining the relationship between humans and artificial intelligence through ownership, privacy, and massive context. In an age of capture and control, HUGE sells independence and sovereignty.