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Google’s $10,000 refund test shows why AI agents need zero trust
Google released an open-source zero-trust reference architecture for AI agents defending against prompt injection via signed database writes, gVisor sandboxing, and deterministic gating.
Google's demonstration, built on the Agent Development Kit (ADK) and Gemini, shows a customer support agent that could be manipulated into issuing a $10,000 refund on a $149 order and exposing environment variables via AI-generated Python. The architecture adds three security layers outside the model: cryptographic signatures on database writes verified via Cloud KMS backed by Cloud HSM, gVisor sandboxing of generated code with network egress disabled, and a Semantic Gateway applying deterministic checks to prompts and tool calls. It treats system prompts as insufficient boundaries because prompt injection, prompt tuning, or model updates can change agent behavior.
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Rapidly scaling online storage to serve over 1 billion ChatGPT users
OpenAI's Habitat online storage platform now handles over 70 million requests per second and 500 PB of data for 1 billion users.
OpenAI details the evolution of Habitat, its online storage platform backing ChatGPT and other products, which began in mid-2024 as a Python client-side library over Azure Cosmos DB. Habitat now processes more than 70 million requests per second, serves over 500 petabytes of data across nearly 40 geographic regions, and supports over 1 billion users weekly. By mid-2025 the client library approach became brittle, so OpenAI moved Habitat into a standalone service to centralize deployments, observability, and multi-tenancy reliability. This is part one of a two-part series; a future post will cover read optimization and scaling the Azure Cosmos DB partnership.
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Benchmark Radar: A Living Database and Search Engine for AI Benchmarks and Evaluation
Benchmark Radar provides a living searchable database of 1,283 AI benchmark records and 12,916 score observations drawn from 37 daily discovery sources.
Benchmark Radar combines daily discovery of benchmark papers, repositories, datasets, and releases from 13 direct connectors and 24 first-party feeds into a searchable catalog with model card mentions and score histories. The catalog contains 1,283 source records drawn from 4 benchmark catalogs plus 12,916 numeric observations on 790 records. The release includes a web dashboard with leaderboard, Pareto frontier of score versus usage, saturation and trend views, daily feeds, a CLI, and reproducible analysis. The paper audits the full catalog and examines benchmark saturation and limits of score comparisons.
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Akamai Valkey Managed Database: Real-Time Memory for Enterprise AI
Akamai launched Valkey Managed Database, a low-latency in-memory data layer aimed at cutting AI inference costs and accelerating RAG.
Akamai introduced Valkey Managed Database, a managed in-memory data service based on the open-source Valkey project. The company positions it as real-time memory for enterprise AI, optimizing inference costs, accelerating retrieval-augmented generation, and powering real-time AI agents.
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The AI Supply Chain Has a Security Problem, and Much of It Is Sitting on the Open Internet
Researchers counted 36,769 publicly reachable self-hosted AI endpoints, only about 2% behind HTTP authentication, exposing Ollama, vLLM, and Flowise to abuse.
A Mysterium VPN study found 36,769 self-hosted AI endpoints reachable through internet scanning, with only 2.02% returning an HTTP authentication challenge. Open WebUI accounted for 18,529 reachable instances, Ollama for 6,935 fingerprinted hosts, and 5,223 agent-builder and workflow platforms were exposed, often holding API keys, database credentials, and other secrets. The report highlights LLMjacking risk from exposed Ollama APIs, a critical Flowise bug (CVE-2026-40933), leaked n8n tokens, and prior SentinelOne/Censys research finding roughly 175,000 exposed Ollama hosts in 130 countries.
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AI Agents Are Here. So Are the Threats.
Unit 42 demonstrates nine framework-agnostic attack scenarios against AI agents built with CrewAI and AutoGen, causing data leakage, credential theft and remote code execution.
Palo Alto Networks Unit 42 investigated how attackers can target agentic applications, implementing two functionally identical apps with the open-source CrewAI and AutoGen frameworks and executing the same attacks on both. Nine attack scenarios produce outcomes including information leakage, credential theft, tool exploitation and remote code execution. Findings show most vulnerabilities are framework-agnostic, arising from insecure design patterns, misconfigurations and unsafe tool integrations rather than flaws in the frameworks themselves. The team published defense strategies per scenario and open-sourced the source code and datasets on GitHub.
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Why the current tech backlash feels different
The Verge's Decoder mailbag discusses the current tech backlash, arguing AI hype overstates verifiability outside software engineering.
Nilay Patel's Decoder mailbag episode addresses listener feedback on the widely discussed 'software brain' essay. He argues AI hype is concentrated on software because code is verifiable through compilation, while domains like drug discovery, math and science lack equivalent verifiability. The episode also touches on AI backlash, surveillance, data centers and upcoming midterm coverage.
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Meta Sued Over Training Data for Its AI and Face-Recognition Systems
Illinois and California families sue Meta alleging Facebook and Instagram photos were used without consent to build faceprints and train AI models.
A proposed class action filed in federal court in Chicago alleges Meta extracted biometric data from Facebook and Instagram photos without notice or consent to develop the unreleased NameTag face-recognition system for smart glasses and to train generative models Emu and Muse Image. Plaintiffs invoke Illinois' Biometric Information Privacy Act, seeking $5,000 per intentional or $1,000 per negligent violation, with a class that could number in the millions dating back to September 2021. Meta previously paid $650 million to settle an Illinois biometric class action in 2020 and $1.4 billion to Texas in 2024. Meta calls the suit without merit and says it is not building a universal face database.
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