Saw a tweet this morning that stopped me mid-scroll 👇 "Sandboxing the agent process is useful. It still doesn't answer the uncomfortable part: what did the agent send out, what policy allowed it, and what proof do you have after the run? That's where agent security gets real." Honestly? Spot on. For the last few months, I've been talking about Docker Sandboxes as the way to keep agents from nuking your laptop. And it works ~ hard VM boundary, only the target workspace mounted, blast radius contained. But sandboxing is the floor, not the ceiling. The uncomfortable questions still remain: • What did the agent send out? → you need network controls • What policy allowed it? → you need MCP + sandbox policy • What proof do you have after? → you need centralized enforcement and audit This is exactly the gap Docker AI Governance is built for. Sandbox + network + MCP controls. Defined once. Enforced across every developer's machine. No migration required. Sandboxing = blast radius. Governance = the receipts. You need both. I'll be diving into this afternoon at the Agentic AI Unplugged meetup in Bengaluru ~ walking through AI Coding Agent Horror Stories, what problem does Docker sandboxes solve, what they don't, and where governance picks up. If you're in Bengaluru, come hang out. 🔗 https://lnkd.in/gWm6tnK8 #Docker #AIAgents #AgentSecurity #MCP #DevRel
Docker Sandboxes Not Enough for AI Agent Security
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I spent the last few months architecting sandboxes for AI agents. The default answer everyone reaches for: Firecracker. The question nobody asks: does your agent actually need a kernel? I wrote a detailed breakdown of the full isolation spectrum from virtual filesystems to microVMs and a framework to pick the right layer based on what your agent actually does. Always looking for feedback from engineers building in this space. https://lnkd.in/gQsCv_nN
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You’ve probably already hit some version of this: A hardened image breaks your build. An agent needs more access than you’re comfortable giving it. A local AI setup works until you try to move it toward production. A trusted package suddenly becomes part of a supply chain incident. These are not separate problems. They are part of the same shift happening across AI workflows, infrastructure, and software supply chains. The latest Docker Navigator issue connects the patterns teams are working through now, from sandbox isolation and local model workflows to hardened images and supply chain attacks. Read in full → https://bit.ly/4tSi3cm
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Supply chain, sandbox isolation, hardened images, and local model workflows look like four different problems. In production AI environments, they keep colliding in the same place, usually at the boundary where a working prototype meets real infrastructure requirements. We've run into this pattern on healthcare and enterprise builds. An agent that works cleanly in a contained setup needs filesystem access or network reach the moment it connects to anything real, and that's where the threat surface opens up. Docker framing all of this as one connected shift is the right read. #AIAgents #EnterpriseAI
You’ve probably already hit some version of this: A hardened image breaks your build. An agent needs more access than you’re comfortable giving it. A local AI setup works until you try to move it toward production. A trusted package suddenly becomes part of a supply chain incident. These are not separate problems. They are part of the same shift happening across AI workflows, infrastructure, and software supply chains. The latest Docker Navigator issue connects the patterns teams are working through now, from sandbox isolation and local model workflows to hardened images and supply chain attacks. Read in full → https://bit.ly/4tSi3cm
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I built an AI agent that earns money, manages its own finances, spawns child agents, and improves itself. Then I spent Phase 5 making sure it doesn't do anything dangerous. Here's what 5 phases of building in public looks like 👇 Phase 1 — Foundation Identity system, encrypted Ethereum wallet, Claude powered content generation, safety guardrails, supervised mode. Phase 2 — Autonomy Cloud infrastructure self-management, graduated risk classifier, autonomous treasury watchdog. Phase 3 — Self-Improvement LLM-powered retrospective analysis, DB backed parameter tuning, sandboxed code patch proposals, A/B testing. Phase 4 — Reproduction The agent spawns child agents when its treasury crosses a threshold, monitors their health, collects revenue share, and terminates underperformers. Phase 5 — Hardening (just shipped) This one was humbling. I found an API key leaking through the management dashboard HTML. Classic. Fixed it. Then I built: 🔒 A full input sanitization layer null-byte injection, XSS, SQL injection, path traversal. All blocked. 🛡️ Security middleware from scratch — CSP, HSTS, per-IP rate limiting, request audit logging 💥 25+ adversarial tests that actively try to break the guardrails 🤖 GitHub Actions CI — every push is type-checked and fully tested before merge 📄 SECURITY.md, CONTRIBUTING.md, CHANGELOG.md — ready for the world 320 tests. 0 failures. The full source is now open on GitHub: 👉 https://lnkd.in/ggFCAzNP This isn't a tutorial project. It's a real attempt to answer: what does a production-grade autonomous AI agent actually look like? If you're building in the agentic AI space follow along. Phase 6 is already forming in my head. #BuildInPublic #AI #AgenticAI #OpenSource #TypeScript #SecurityEngineering #LLM #Claude
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5 issues we keep finding in AI-generated codebases: -Heavy objects initialized inside loops (memory pressure + throughput issues) -Redundant LLM calls where caching should exist (latency + cost) -Hardcoded configs and secrets (security risk) -Disk-heavy processing patterns (I/O bottlenecks + storage spend) -No documentation — AI writes code, not context Cheap to build. Expensive to scale. We offer a free 3-hour audit that surfaces your top critical issues. Link in Featured ↓ #CodeAudit #TechnicalDebt #AIcoding #DevOps #StartupEngineering
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🛡️ 𝗜 𝗳𝗼𝘂𝗻𝗱 𝗮 𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗴𝗮𝗽 𝗶𝗻 𝗺𝘆 𝗼𝘄𝗻 𝗼𝗽𝗲𝗻-𝘀𝗼𝘂𝗿𝗰𝗲 𝗽𝗿𝗼𝗷𝗲𝗰𝘁. 🔍 v1.5.0 just dropped - and here's the uncomfortable truth that drove it → My Kubernetes (Official) live-guard agents in Vanguard Frontier Agentic had 6 agents making live cluster mutations. Prompt-level rules only. No RBAC manifests. No kubectl pre-flight checks. No admission control. Anyone running them was relying on the AI to behave correctly. That's not security. That's hope. 𝘃𝟭.𝟱.𝟬 𝘀𝗵𝗶𝗽𝘀 𝗮 𝟱-𝗹𝗮𝘆𝗲𝗿 𝗱𝗲𝗳𝗲𝗻𝘀𝗲 𝗺𝗼𝗱𝗲𝗹 𝗮𝗰𝗿𝗼𝘀𝘀 𝗲𝘃𝗲𝗿𝘆 𝗹𝗶𝘃𝗲-𝗴𝘂𝗮𝗿𝗱: → L1 - Prompt rules and HARD REFUSE lists per agent → L2 - Explicit allowed-tools declarations (least privilege) → L3 - Deny-by-default Kubernetes RBAC + kubectl auth pre-flight → L4 - Kyverno ClusterPolicies + ValidatingAdmissionPolicies → L5 - kind-based CI regression matrix across K8s 1.28-1.31 The best part? A Codex bot review caught a real P1 bug before merge. A coredns ClusterRole scope misconfiguration. resourceNames on a ClusterRole doesn't restrict by namespace. One bad binding and your agent can patch any ConfigMap named "coredns" cluster-wide. A privilege escalation vector hiding inside a least-privilege config. That's the kind of gap that gets missed in every manual review. Automated adversarial review caught it. The truth is... "the AI will behave" is never a security model. v1.5.0 also ships a new read-only network architecture review agent - CNI choice, Gateway API, CoreDNS, ClusterMesh topology, egress. Wired into the kubernetes-maestro orchestrator for parallel dispatch. Now at 140 skills. 143 agents. --- 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀: → GitHub - https://lnkd.in/dkzEJjzX → skills.sh - install all 140 skills directly --- Are your AI agents enforcing least privilege at the cluster level - or just trusting the prompt? #Kubernetes #CloudSecurity #OpenSource #DevSecOps #RBAC #AIAgents #MultiCloud #ZeroTrust #SecurityByDesign #CloudArchitecture
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One command turns any open-source repo into an AI agent backdoor. OpenClaw proved no supply-chain scanner has a detection category for it https://lnkd.in/ej2EW4dW #opensource #securebydesign #softwarecomposition VentureBeat
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PyTorch Lightning supply-chain compromise puts AI developer credentials at risk is the kind of story that deserves a closer defender read. Sometimes they land inside routine developer workflows, where teams trust package registries and CI automation to keep moving. This is a good reminder that security teams need to look beyond the headline and focus on operational impact.
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Day 89/90 — Built an AI agent that doesn't just diagnose problems. It fixes them. 🔧 KubeHealer scans your Kubernetes cluster, sends broken pod details to Claude, and proposes targeted fixes — then waits for your approval before touching anything. I deployed 3 intentionally broken apps: → web-app: image typo (ngnix instead of nginx) → ImagePullBackOff → memory-app: 1Mi memory limit → OOMKilled → config-app: missing ConfigMap → CreateContainerConfigError The agent fixed the first two automatically after I approved. For the third? It said "I can't fix this — escalating to human." That's the right answer. Then I tested crash recovery: Killed the worker mid-diagnosis. Restarted it. Temporal replayed every completed step from history and resumed exactly where it left off — approval prompt and all. This is what production AI agents look like: ✅ Human approval before any change ✅ Audit trail of every decision ✅ Crash-resistant with Temporal ✅ Knows when to escalate vs when to act This is AIOps in 2026. #90DaysOfDevOps #DevOpsKaJosh #TrainWithShubham #AIOps #Kubernetes #AgenticAI
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Is your AI stack becoming a single point of failure? 1. 🔀 157,000 developers are now using OpenCode to hedge against Anthropic lock-in. If your team builds on one AI provider, you already know the risk. Multi-model setups are quickly becoming the new standard for serious engineering teams. 2. 📊 Prometheus quietly missed Cilium metrics at 2 a.m. and nobody noticed until things broke. A reminder that "green dashboards" do not always mean healthy systems. Check your scrape configs and label drops before they check you. 3. 📎 Claude can now follow users across Outlook, Word, Excel, and PowerPoint. For DevOps folks, this means more AI traffic inside corporate networks and new questions around data flow, audit logs, and access control. 4. 🐛 Anthropic launched a HackerOne bug bounty program for its models and infra. Good news if you do security research, and a useful signal that AI vendors are starting to treat safety like real production software. The AI layer is becoming part of the stack we have to operate, monitor, and secure like everything else. How is your team handling AI vendor risk right now, one provider or several?
