Ship It Weekly – DevOps, SRE, and Platform Engineering News with Brian Teller

This week on Ship It Weekly, an attacker used Qubole's trusted access into customer AWS accounts to read secrets and pull whole S3 buckets. Claude Code's "don't ask again" button has been saving passwords and API keys into .claude/settings.local.json, a file that keeps turning up on GitHub and Docker Hub. At KubeCon, the CNCF took in Agent Substrate and OpenShell as Sandbox projects, and AWS launched Nitro Sandboxes for EKS. And WorkOS traced a breach of an internal sales tool to a security researcher's AI agent that escaped its test environment.

The thread: what can it touch? A vendor role nobody removed, an approval that saved the secret with the command, agents running unreviewed code, and an internet-facing tool with a guessable dev credential.

In the lightning round: an exploited Outlook bug (CVE-2026-124194) and a 9.9 Azure DevOps Server RCE in Patch Tuesday, N-able patching the Able2Shell exploits in N-central, KubeVirt's CNCF graduation, and OpenShift 5 going GA.

The human closer comes from Brent Chapman: incident resolution time isn't something you should target. Target what you can change, and track the rest.

Ship It Weekly is a 2026 DevOps Dozen finalist for Best DevOps Community Program or Event, and I'm a finalist for Enterprise DevOps Champion of the Year. The public vote is open through December 31.

Highlights

• Why the External ID didn't stop a stolen vendor credential

• Find every role that trusts an outside account, and delete the idle ones

• settings.local.json: rotate, untrack, and add it to .dockerignore

• Moving an agent's boundary off the shared kernel

• WorkOS's own list: internet-facing API, dev credential, no rate limit

• Target the steps you control, not resolution time

Links

Qubole cross-account access abused (Wiz)

Claude Code's "don't ask again" file leaks secrets (The Hacker News)

settings.local.json secrets on GitHub and Docker Hub (GitGuardian)

Agent Substrate and OpenShell join the CNCF

Nitro Sandboxes for Amazon EKS (AWS)

WorkOS incident update

November 2026 Patch Tuesday (Rapid7)

N-central and Able2Shell (Huntress)

KubeVirt graduates (CNCF)

Red Hat OpenShift 5 (Virtualization Review)

Why incident resolution time isn't something you should target (Brent Chapman)

Vote in the 2026 DevOps Dozen

This week's On Call Brief

More episodes and show notes

On Call Brief

LMGT Awards

This is a guest conversation episode of Ship It Weekly, separate from the weekly news recaps.

In this Ship It Conversations episode, I talk with Mallory Haigh about why context alone isn't enough for AI agents, the harness and governance layers that actually run them, and why she tells some platform teams to pump the brakes before adding agents at all.

Mallory is Head of Platform Education and Advocacy at PlatformEngineering.org. She teaches in Platform Engineering University and coaches platform teams through Platform Engineering Consulting, where the job gets jokingly called "platform therapist".

We start with her New York City analogy. Drop someone in the middle of the city with no directions and they'll get lost, and an agents.md file on its own does the same to an agent. It also needs to know what to do, how to do it, how it'll know it's done, and what it's allowed to touch. Mallory splits that into the harness, which is how the agent does the work, and governance: who and what is allowed, what's safe, and how it's watched.

Then we get into IDPs as context layers. Mallory says that's partly right, because the IDP and its golden paths are the foundation. What's missing are path specifications. Probabilistic paths are agent-driven, like a read-only PR review. Deterministic paths are your pipelines, like a CI build. Hybrid paths pair an agent with a gate: the agent opens the PR, CI validates it, the agent retries, and a human makes the final call. Cap those retries at three to five, and if an agent always does the same thing with the same outcome, ask whether it should be an agent at all.

On governance, Mallory says platform engineering is a practice about building trust, and that in the last three weeks alone she's told multiple platform teams to pump the brakes on agents because they can't get human governance right yet. We cover agent identity (specialists, not generalists, built on the RBAC you already have), agents watching terabytes of logs a day for SRE teams, and why every team now has to write down the tribal knowledge it never documented.

Her advice on where to start: ask why. Then try a read-only PR review agent, move to a hybrid validate-change path, and remember the line from one of her PlatformCon New York workshops: if we can't even write a README for our repos, how are we going to write one for our agents?

Highlights

• Why context delivery alone isn't enough for AI agents

• The harness and governance layers that make up agent infrastructure

• Observability for agents: token spend, time to completion and retry loops

• Probabilistic, deterministic and hybrid paths

• Retry limits, and when an agent should be a checklist instead

• Pumping the brakes when human governance isn't there yet

• Agent identity, RBAC and specialist agents

• Where a platform team should start: ask why, then a read-only PR review agent

Links

Mallory on LinkedIn · PlatformEngineering.org · Platform Engineering University · Platform Engineering Consulting · Thinking in Platforms · More episodes · On Call Brief · LMGT Awards

This is a guest conversation episode of Ship It Weekly, separate from the weekly news recaps.

In this Ship It Conversations episode, I talk with Ryan Packer about why a CTO is writing code again, what happens to CI/CD when every engineer runs a fleet of agents, and who trains junior engineers when AI can fix the bugs.

Ryan is the CTO at BambooHR and a former VP of Engineering at Adobe. He's watched engineering organizations change from Omniture to Adobe to BambooHR, and he says he's spent more time coding this year than in the rest of his career combined.

We start with why. Ryan will never write production code for his team again, but he wants to use the same tools and learn the same techniques they're learning, and to show an idea in working code instead of a PRD or a Slack message. He's also honest about the limits: his sandbox serves maybe a hundred people, and his team's software serves millions.

Then we get into the software factory. In Ryan's own version, a customer files a bug, an agent opens the PR, tests it for security and performance, scores it, and ships it if it meets the bar. At BambooHR, pull requests are up almost 80% since the start of the year, and quality has improved by the metrics they track. The part he's most excited about is more at-bats with customers: an idea to a working prototype in a day.

For platform teams, Ryan says AI has been more of a challenge than a help for CI/CD. BambooHR was already releasing hundreds of times a day, but agents do their best work when they can run and test what they build. Ten, twenty or a hundred agents per engineer means environments that spin up and down fast, with real data. We also cover protecting HR and payroll data: terms with the frontier labs, data retention, and an AI council.

On careers, Ryan thinks mid-career engineers face the hardest change, and that the ones who keep learning have little to worry about. On junior engineers, he doesn't claim a perfect answer. He no longer needs them to fix bugs, so he suggests making the LLM explain its choices and using an adversarial agent that lists five things it would have done differently. We finish with every engineer managing a team of agents, small pods doing what 10 to 15 people used to, and why he expects the same or more engineers a year from now.

The big takeaway: if every project in your portfolio has better than a 50% chance of success, you're not being ambitious enough. Put something in there with a 10% chance.

Highlights

• Why a CTO is coding again, and why his sandbox isn't production

• The software factory: a customer bug to an agent PR to a tested release

• Pull requests up almost 80%, with quality improving

• More at-bats with customers: idea to working prototype in a day

• Why AI is straining CI/CD and test environments

• Protecting HR and payroll data while adopting AI

• Junior engineers, adversarial agents and making the LLM explain itself

• Every engineer with a team of agents, and the 10% project

Links

Ryan on LinkedIn · BambooHR · BambooHR careers · More episodes · On Call Brief · LMGT Awards

This week on Ship It Weekly, GitHub will block pull_request_target in public repos by default from December 2, with brownouts on Mondays, November 9 and 16, 11 AM to 1 PM ET. A WordPress core update race (CVE-2026-119977) is on CISA's exploited list: during a full-package update, a fresh installer can sit for a moment at a public URL under wp-content/upgrade. A hijacked DebugBear script drained about $2.7 million from Polymarket users after DebugBear's DNS was compromised through its registrar. And Alpine Linux explains why upgrading GitLab didn't fix CVE-2026-97229 on its own image: the fix was a patch to Ruby.

The thread: what actually ran, as opposed to what you think ran. A trigger that runs a fork's code with your secrets, an update that opens its own window, a script served by whoever controls the DNS, and a version number that said "fixed".

In the lightning round: OpenSSH 10.6p2 fixes a password login bypass on musl builds with PAM off, which is how Alpine builds openssh-server; Debian and Ubuntu patch an APT package swap (CVE-2026-109518); and .NET 8 and 9 reach end of support on Tuesday, November 10.

The human closer comes back to Alpine's "we cannot rule it out." If an attack hit you this week, could your logs prove it didn't work?

Highlights

• pull_request_target: what it runs, and what to check before Monday

• WordPress: when keeping current is what opens the window

• DNS, HTTPS and subresource integrity for third-party scripts

• Registrar lock, MFA and alerts on DNS changes

• Alpine: GitLab from your own image needs a fixed Ruby too

• "No evidence" is only as good as the logs you kept

Links

pull_request_target blocked by default (GitHub): https://tsn.io/KwebM

WordPress advisory GHSA-4w3m-c66w-69mg: https://tsn.io/1HGiK

WordPress upgrade-window RCE (Wordfence): https://tsn.io/Pc6X2

Polymarket postmortem: https://tsn.io/rOc2M

DebugBear on the registrar DNS compromise: https://tsn.io/4BjIg

Polymarket losses and refunds (Hackread): https://tsn.io/qNhoe

Alpine's GitLab security incident: https://tsn.io/GpNoR

OpenSSH 10.6p2 release notes: https://tsn.io/dQHbG

Alpine openssh-server advisory: https://tsn.io/lExh2

APT CVE-2026-109518 (Debian DSA-6607-1): https://tsn.io/LSjTb

.NET 8 and 9 end of support: https://tsn.io/7NC5R

This week's On Call Brief: https://tsn.io/XJLrm

More episodes and show notes: https://tsn.io/NqkdP

On Call Brief: https://tsn.io/Gpz2d

LMGT Awards: https://tsn.io/lAERA

This is a guest conversation episode of Ship It Weekly, separate from the weekly news recaps.

In this Ship It Conversations episode, I talk with Davlet Dzhakishev about what it takes to let AI agents work on your cloud infrastructure, why guardrails belong in the architecture and not the prompt, and what running your own agents really costs.

Davlet is the CEO and co-founder of Cloudgeni, which builds AI agents for cloud infrastructure. He got into this after working through SOC 2 and ISO 27001 compliance prep mostly by hand, and asking what agents would need to fix those problems reliably without making things worse.

We start with the incident that changed how Cloudgeni builds. An agent hit a snag on a simple Terraform change and decided the fix was to run apply. On staging. Resources got recreated, and Davlet calls it his wake-up call. Since then, guardrails come first: sandboxed runtimes, scoped credentials, read-only cloud access, and changes that go through Git, CI/CD and tests. At the architecture level, the agent shouldn't be able to do what you don't want it to do.

Then we get into agentic PR reviews, which check human and agent work the same way, and why a good reviewer catches more than the obvious errors. Davlet's example is a private network around a key vault: great for security, until your own apps lose access. We also cover drift, and why you need to reconcile it before you can trust agent PRs.

On cost, Davlet's view is that AI subscriptions are heavily subsidized, and running your own agents on API tokens can cost ten to twenty times more. Most agent usage also has no audit trail, so a stuck loop can poll a dead API server every day and no one will know. He thinks FinOps for AI is going to be big, orphaned agents and all. That need for audits is part of why Cloudgeni open-sourced its foundation as OpenGeni.

We finish with what he still wouldn't hand to an agent, and where a platform team should start without building a monster: PR reviews first, then agents writing IaC from your templates, then automation. His starter kit is a real sandbox, ideally in the cloud, because Docker doesn't count. Give it read-only access to an AWS account or Azure subscription, one IaC repo, and permission to open PRs. Ask it to delete a resource, and the worst it can do is open a PR.

The big takeaway: a safe agent is mostly a permissions problem, not a prompting problem. Give it read-only access and PR-only changes, and it can't make that mistake however you word the request.

Highlights

• The Terraform apply on staging that became his wake-up call

• Guardrails at the architecture level, not in the prompt

• Read-only access, with every change through Git, CI/CD and tests

• Agentic PR reviews that catch operational impact

• Why drift has to be reconciled before you can trust agent PRs

• Why API tokens can cost 10 to 20 times more

• FinOps for AI, stuck loops and orphaned agents

• A starter kit: a real sandbox, read-only cloud access, one IaC repo, PRs only

Links

Davlet on LinkedIn · Davlet on X · Cloudgeni · OpenGeni · Agents guide · More episodes · On Call Brief · LMGT Awards