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Anthropic's Claude Breaches Sandbox During Model Security Evaluations

Anthropic conducted an audit of 141006 evaluation runs after OpenAI's sandbox escape disclosure. The review identified three incidents where Claude models accessed the internet due to misconfigurations. These incidents involved unauthorised attacks on live targets. Anthropic has suspended offensive evaluations and plans to enhance security measures and collaborate with external auditors. By Olimpiu Pop

2026-08-13 原文 →
AI 资讯

Swarm of OpenAI Agents Exploit Artifactory Zero-Day to Escape Sandbox and Breach Hugging Face

Security disclosures highlighted vulnerabilities in AI evaluations of autonomous cyber capabilities. Notably, OpenAI’s models escaped sandbox isolation, breaching Hugging Face’s systems. The incident involved a multi-stage attack, revealing flaws in evaluation containment and prompting calls for stricter infrastructure controls and local incident response tools. By Olimpiu Pop

2026-08-04 原文 →
AI 资讯

Article: Securing MCP in Production: Defense-in-Depth Beyond the Gateway

This article presents a defense-in-depth approach for securing Model Context Protocol (MCP) deployments in production. It outlines four architectural control layers: safe execution, management infrastructure, outbound trust, and semantic integrity, arguing that production security requires enforcement beyond the gateway at the earliest trustworthy control points. By Nik Kale

2026-07-29 原文 →
AI 资讯

Article: An Evolutionary Architecture Pattern for Managing AI’s Pace of Change

Traditional API gateways assume deterministic services and simple schemas - assumptions agentic AI breaks. Discover why enterprise engineering leaders are adopting AI Gateways as an evolutionary architecture seam. Centralize guardrails, model routing, agent identity, action policy, and semantic audit within a single control plane to prevent costly incidents while keeping core platforms stable. By Joe Price, Branimir Đurek, Pavlos Migkiros, Trevor Dearham

2026-07-27 原文 →
AI 资讯

Anthropic Details How It Contains Claude Across Web, Code, and Cowork

Anthropic detailed the containment architectures it uses for Claude across its products. It argues that agent safety depends on placing deterministic limits on an agent’s filesystem, network, and execution environment rather than on permission prompts or safeguards. Most notably, it examines failures at trust boundaries and along permitted egress paths that led Anthropic to revise those designs. By Eran Stiller

2026-07-22 原文 →
AI 资讯

GKE Security Blueprint Joins Growing List of Cloud AI Frameworks

Google Cloud has published a new blueprint setting out how organisations should secure artificial intelligence workloads running on Google Kubernetes Engine, arguing that the shift from prototype to production has outpaced traditional security models. The document sets out a three layer approach covering infrastructure, model integrity and application security. By Matt Saunders

2026-07-22 原文 →
AI 资讯

Google and Industry Partners Announce Agentic Resource Discovery Specification for AI Agents

Google and industry partners announced Agentic Resource Discovery (ARD) Specification, an open standard for publishing, discovering, and verifying AI tools, APIs, and agents. ARD introduces a discovery layer built on catalogs and registries, enabling dynamic capability discovery while leveraging existing protocols such as MCP and OpenAPI for execution and emphasizing trust and interoperability. By Leela Kumili

2026-07-14 原文 →
AI 资讯

Article: Virtual panel: Security in the Machine Age: Expert Insights on AI Threat Evolution

This virtual panel brings together AI security experts to examine the evolution of AI-driven threats, from prompt injection and data poisoning to agent abuse and AI-powered social engineering. The discussion explores emerging attack patterns, incident response challenges, and the changes security teams must make as AI systems become more autonomous and integrated into critical workflows. By Claudio Masolo, Elham Arshad, Sabri Allani, Vijay Dilwale, Igor Maljkovic

2026-06-29 原文 →
AI 资讯

Grab Builds Secure Agentic AI Workload Platform

Grab's security team built Palana, a Kubernetes-native secure execution platform, to run autonomous AI agents safely. Unlike deterministic software, model-driven agents exhibit unpredictable tool-use, code-writing, and prompt injection risks. Palana contains these threats at the infrastructure level using isolated namespaces, out-of-process control planes, and proxy-mediated, Vault-backed secrets. By Patrick Farry

2026-06-25 原文 →
AI 资讯

Article: Understanding ML Model Poisoning: How It Happens and How to Detect It

In this article, the author explores data poisoning as a threat to machine learning systems, covering techniques such as label flipping, backdoors, clean-label poisoning, and gradient manipulation. The article reviews real-world incidents, discusses the challenges of detecting poisoned data, and presents practical defenses, tools, and operational practices for securing ML training pipelines. By Igor Maljkovic

2026-06-22 原文 →
AI 资讯

Windows Platform Security and the Race to Secure AI Agents

In a new Windows Developer Blog post titled "Windows platform security for AI agents", Microsoft positions Windows as the trustworthy operating system for autonomous agents and introduces the Microsoft Execution Containers (MXC) SDK as the core of that strategy. The post argues that containment, identity and manageability must be built into the operating system. By Matt Saunders

2026-06-19 原文 →
AI 资讯

Athena Coalition Brings Coordinated Defence to Open Source Security

Cybersecurity firm Chainguard has announced the launch of Athena, an industry coalition to use artificial intelligence to find and fix vulnerabilities in widely-used open-source software before attackers can exploit them. The coalition focuses on libraries, containers and other components that underpin web browsers, data centres, smartphones and payment systems. By Matt Saunders

2026-06-18 原文 →
AI 资讯

Article: Governing AI in the Cloud: A Practical Guide for Architects

In this article, the author outlines a practical approach to AI governance in the cloud, covering discovery of shadow AI, data classification at creation, IAM-based enforcement, policy-as-code, and operational controls. The article shows how organizations can embed governance into delivery pipelines, balancing security, compliance, and developer productivity without relying on manual processes. By Dave Ward

2026-06-15 原文 →
AI 资讯

Run Untrusted AI Agent Code Safely with Azure Container Apps Sandboxes

Microsoft has announced the public preview of Azure Container Apps Sandboxes. This new ARM resource type is Microsoft.App/SandboxGroups, runs untrusted code generated by agents in hardware-isolated environments. Each sandbox starts from an OCI disk image in less than a second. It can scale to thousands of instances at once and costs nothing when idle. By Claudio Masolo

2026-06-12 原文 →