Toward Zero-Copy OCI Layers
How Kalahari turns OCI image layers into page-aligned EROFS images, hands them to the guest as virtio-pmem with DAX, and lets overlayfs do the stacking.
Technical writing on AI agent security, capability-based authorization, and the infrastructure gap between identity, orchestration, and choreography.
How Kalahari turns OCI image layers into page-aligned EROFS images, hands them to the guest as virtio-pmem with DAX, and lets overlayfs do the stacking.
A DAX altmap kernel paging fault in fs/dax and a 7-year-old ESTALE bug in overlayfs copy-up: two upstream Linux bugs surfaced while building Kalahari.
How Kalahari's guest kernel boots in milliseconds: starting from allnoconfig and turning on only the devices the microVM's VMM actually exposes.
Kalahari is an agent sandbox SDK that runs OCI images in a microVM on Linux (KVM) and macOS (Hypervisor.framework), with no remote service required.
How Kalahari maps common ComputeSDK, E2B-style, and Daytona-style process and filesystem workflows onto one native sandbox lifecycle.
Why Kalahari uses Mach memory entries, mach_vm_map(copy=TRUE), Mach IPC port descriptors, and registered-port bootstrap to make VM branching portable.
How Kalahari turns sandbox network options into VM device state through usernet, packet policy, packet leases, and scoped wake tokens.
Why Kalahari's host-guest IPC is an SPSC ring buffer over shared memory instead of vsock, and what Firecracker and SmolVM say about where vsock state lives.
How Kalahari keeps process handles, PTYs, callbacks, and zygote boundaries coherent across parked VM runs.
How Kalahari narrows snapshot and restore correctness with typed topology, queue counts, memory geometry, VM state views, and architecture-specific exits.
How Kalahari's virtio layer turns descriptor ownership into completion tokens, queue brands, deferred writable regions, and exact used-ring lengths.
Why zygote-style VM spawning is less about copying memory and more about establishing that every device, backend, and guest channel is quiet enough to freeze.
A secure execution environment that lets AI agents write and run code safely—with 98% fewer tokens than tool-call loops.
Salesforce patched a critical prompt injection vulnerability in Agentforce. The attack chain reveals why input filtering alone can't secure agentic AI—and what actually works.
A critical CVE in LangChain shows why credential isolation matters more than perfect code.
Non-Human Identity (NHI) is necessary hygiene, but it can't solve the authority-flow problems in multi-agent systems. The right unit is the transaction, and the right primitive is capability-based authorization.
Akto surveyed 100+ CISOs and security leaders. The findings: agents are in production, but inventory, governance, and runtime controls are missing. The gap is now measurable.
OS history solved process isolation structurally, not behaviorally. Agents need the same treatment: a kernel that controls what they can see and do.
A new paper proposes defense-in-depth for MCP security. The diagnosis is right, but policy enforcement can't solve what structural isolation must.
The Copilot vulnerability that allowed silent file access exposes a structural flaw: ambient authority plus bolt-on audit logging. Capability chains make that class of bug impossible.
Default agent memory patterns leak unless you enforce scoping at the runtime boundary. The problem isn't implementation bugs—it's architectural.
Cloudflare's December 2025 resilience report reveals what every zero-trust org learns: your security stack becomes the outage when the platform is on fire.
A critical vulnerability in mcp-remote affected 558,846 downloads. The bug was client-side, but the attack exploited OAuth dynamic discovery—a trust assumption that breaks for autonomous agents.
API keys in prompts, env vars, or code turn agents into confused deputies. Here is the safer pattern.
Enterprise agents that demo autonomous refunds ship with 'click to approve' buttons. Here's why—and what changes when authorization is solved.
What capabilities are, how they differ from ACLs, and why they matter for AI agent security—but also why capabilities alone aren't enough.
A 1988 security paper predicted why AI agents are vulnerable. The standard fix is incomplete.
GitHub's security principles minimize autonomy to minimize risk. But what if you could maximize autonomy within cryptographic bounds?
Everyone's optimizing what agents know. Nobody's solving what agents are permitted to do. The context engineering revolution is incomplete without trust.
AgentVigil achieved 70%+ attack success rates against o3-mini and GPT-4o agents—with all defenses active. Linguistic defenses are necessary but insufficient.
The Model Context Protocol solves agent-to-tool communication. But who authorized the agent to use that tool, with what constraints, for which transaction?
1,340 practitioners surveyed. 57% have agents in production. Security ranks as the #1 concern for large enterprises. What this means for agent infrastructure.
How capability-based authorization maps to regulatory requirements—and what auditors actually need to see.
A threat taxonomy for multi-agent systems—and where traditional security controls struggle.
Insurance isn't just a convenient example—it's a perfect example. It exposes exactly why existing security models break.
How a referral agent proves authorization without sharing credentials—and why OAuth can't do this.
Springer just published the most comprehensive treatment of agentic AI security. It validates the problem. It acknowledges the gap. It does not fill the gap.
Traditional security asks what you possess. Capability security asks who you are in the transaction.
New research quantifies the chaos: uncoordinated agents amplify errors 17x. The question is who builds the guardrails.
Identity providers solve 'who is this agent?' Orchestration platforms solve 'what should this agent do?' But what solves 'what can this agent actually do right now, in this transaction?'
When the architect of a $150B stablecoin calls for cryptographic agent credentials, the market is sending a signal.
The #1 AI lab just told enterprises how to build agents. They forgot to explain how to secure them.