Space secrets security update

Security incident disclosure — July 2026

An update on our safety & security practices

An update on our safety & security practices

Hardware Keystores for AI Agent Signing Workflows: A Zero-Trust MCP Enforcement Architecture

arXiv:2608. 06130v1 Announce Type: cross Abstract: AI agents performing cryptographic operations (signing Git commits, authenticating API calls, issuing certificates) currently store private keys in software-accessible locations: plaintext files, environment variables, or container memory.

2024 Security Feature Highlights

How Hugging Face Scaled Secrets Management for AI Infrastructure

STAR-FL: Secure Federated Learning with Spatial-Temporal Analysis and Robust Aggregation

arXiv:2608. 14861v1 Announce Type: cross Abstract: Data poisoning attacks pose serious security threats to Federated Learning (FL) systems in Computer Vision.

Privacy-Preserving AI Verification via Minimal Information Disclosure

arXiv:2608. 02774v1 Announce Type: cross Abstract: AI verification crosses a trust boundary: a verifier must learn enough to establish an authorized claim, yet the same evidence can reveal sensitive details about the model, workload, or hardware.

ShareLock: A Stealthy Multi-Tool Threshold Poisoning Attack Against MCP

arXiv:2606. 27027v1 Announce Type: cross Abstract: With the rapid evolution of LLM-driven agents, Model Context Protocol (MCP), an open protocol bridging LLMs with external tools, has quickly become foundational to modern agent ecosystems.

Trusted access for the next era of cyber defense

OpenAI expands its Trusted Access for Cyber program, introducing GPT-5. 4-Cyber to vetted defenders and strengthening safeguards as AI cybersecurity capabilities advance.

Hide and Seek in Embedding Space: Geometry-based Steganography and Detection in Large Language Models

arXiv:2601. 22818v2 Announce Type: replace-cross Abstract: Fine-tuned LLMs can covertly encode prompt secrets into outputs via steganographic channels.

dstack-capsule: Pod-Level Remote Attestation for Confidential Workloads on Kubernetes

arXiv:2606. 03323v1 Announce Type: cross Abstract: The rise of LLM-as-a-Service and other confidential cloud workloads demands cryptographic proof that user data is processed in a trusted, untampered environment.

A Security Review of Gradio 5