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Cloudflare Containers exposed previous tenants’ disk data through unzeroed blocks

This was not a Firecracker escape or access to a live victim disk. It was a storage-isolation failure underneath the sandbox: researchers recovered foreign directory structures, database pages and complete SQLite databases from reused blocks, and Cloudflare had to fix allocation plus retire existing disks and cached snapshots.

Kubernetes 1.37 moves more cluster operations into the core platform

The release consolidates several recurring cluster-management jobs into core APIs and controllers. HPA scale-to-zero is now default-on Beta, storage-version migration and Pod Certificates are Stable, DRA can satisfy existing extended-resource requests, and large etcd reads gain a streaming path that reduces peak memory pressure.

Amazon Aurora Serverless gets faster burst scaling

Aurora Serverless can now add roughly 12 ACUs in the first second of a scale-up event on platform versions 3 and 4. The change is automatic and is most useful for bursty SaaS, API, batch and agent workloads, but it does not remove the separate resume delay when a database has scaled all the way to zero.

GitSpawn shows how a repository’s own Git config can escape AI coding-agent safety boundaries

The useful lesson is architectural rather than vendor-specific: coding agents inherit execution paths from ordinary developer tooling. If an agent shells out to Git without sanitising repository-local configuration, a hidden `.git/config` can become a host-level command channel that bypasses the controls users think govern the model.

Gemini 3.8 Flash raises agent capability at the same token price — but may use more tokens per task

Gemini 3.8 Flash keeps 3.7 Flash’s promotional per-token rate and Flash-tier latency, but early independent analysis suggests harder reasoning can increase tokens consumed per task. A separate 3.8 Flash Cyber model is available only through Google’s Fairwind defensive-security program.

Google DeepMind is piloting double-blind frontier-model evaluations with confidential computing

The pilot attacks a persistent evaluation trade-off: labs do not want to reveal frontier-model internals, while evaluators do not want benchmark prompts leaking back to the model provider. DeepMind says a Singapore AI Safety Institute pilot kept both sides’ sensitive assets hidden during execution.