Postmortem: How a Bug in Pulumi 3.100 Deleted Our GCP Cloud Run Services

At 14:32 UTC on March 12, 2024, a single Pulumi 3.100 up command deleted 14 production GCP Cloud Run services serving 2.1M daily active users, costing $47k in lost revenue and 4 hours of full outage. We’re sharing every line of code, every metric, and every mistake so you don’t repeat this.

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Key Insights

• Pulumi 3.100’s GCP Cloud Run provider regression triggered mass deletion when service labels contained dots (.)
• Regression introduced in pulumi-gcp v7.21.0, shipped with Pulumi CLI 3.100.0 on 2024-03-10
• Total incident cost: $47k lost revenue + 120 engineering hours, mitigated by 15-minute rollback to Pulumi 3.99.2
• 80% of IaC teams will face similar untested provider regression risks by 2025 without strict version pinning

Incident Timeline: 4 Hours of Chaos

Our team had been using Pulumi for 3 years to manage 142 GCP resources across 6 environments, with zero major incidents. On March 10, 2024, Pulumi announced CLI 3.100.0 with new GCP IAM role binding features we’d been requesting for 6 months. We followed our standard upgrade process: tested the new version in our staging environment (2 Cloud Run services, no dot-containing labels) for 48 hours, saw no issues, and scheduled the production upgrade for March 12 during a low-traffic window.

Staging validation was incomplete: our staging Cloud Run services used labels without dots, so the regression in pulumi-gcp v7.21.0 (bundled with Pulumi 3.100.0) didn’t trigger. At 14:30 UTC, our CI/CD pipeline ran pulumi up for the production stack. The Pulumi output showed 14 resource updates, but the output was truncated in GitHub Actions, so we missed the 14 delete operations planned for our Cloud Run services. By 14:34 UTC, all 14 production services were deleted. PagerDuty alerted at 14:35 UTC for 5xx error rates exceeding 90% across all user-facing APIs.

Our incident response team identified the Pulumi deployment as the root cause within 8 minutes. We rolled back the Pulumi CLI to 3.99.2 via GitHub Actions cache, re-ran the deployment, and restored all services by 18:32 UTC. Post-incident analysis of GCP audit logs confirmed all 14 services were deleted by the Pulumi service account at 14:32 UTC. Pulumi’s engineering team acknowledged the regression 2 hours after our report, shipped a hotfix in 3.100.1 within 8 hours, and credited $50k in platform credits to our team.

Code Example 1: Vulnerable Production Cloud Run Definition

This is the exact Pulumi TypeScript code used in production that triggered the deletion bug. All 14 services used labels with dots, which caused the pulumi-gcp 7.21.0 provider to miscompute resource state.

// Production Pulumi stack: prod-us-central1
// Pulumi CLI version: 3.100.0
// pulumi-gcp version: 7.21.0

import * as pulumi from '@pulumi/pulumi';
import * as gcp from '@pulumi/gcp';

// Load stack configuration
const config = new pulumi.Config();
const projectId = config.require('gcp:project');
const region = config.require('gcp:region') || 'us-central1';
const serviceAccountEmail = config.require('serviceAccountEmail');

// Common labels: dots trigger Pulumi 3.100 bug
const commonLabels = {
    'team.platform': 'backend',
    'env.prod': 'true',
    'cost-center': '1234',
    'service.version': '1.2.3',
};

// Create Cloud Run service with error handling
async function createCloudRunService(
    name: string,
    image: string,
    envVars: Record
): Promise {
    try {
        const service = new gcp.cloudrun.Service(name, {
            name: `${name}-svc`,
            location: region,
            project: projectId,
            template: {
                spec: {
                    containers: [{
                        image: image,
                        envs: Object.entries(envVars).map(([key, value]) => ({
                            name: key,
                            value: value,
                        })),
                        resources: {
                            limits: {
                                cpu: '2',
                                memory: '4Gi',
                            },
                        },
                    }],
                    serviceAccountName: serviceAccountEmail,
                },
                metadata: {
                    labels: commonLabels,
                    annotations: {
                        'autoscaling.knative.dev/maxScale': '10',
                        'run.googleapis.com/cpu-throttling': 'false',
                    },
                },
            },
            metadata: {
                labels: commonLabels,
            },
            traffics: [{
                percent: 100,
                latestRevision: true,
            }],
        });

        // Public access IAM binding
        new gcp.cloudrun.IamMember(`${name}-public-access`, {
            service: service.name,
            location: region,
            project: projectId,
            role: 'roles/run.invoker',
            member: 'allUsers',
        });

        return service;
    } catch (error) {
        pulumi.log.error(`Failed to create Cloud Run service ${name}: ${error.message}`);
        throw error;
    }
}

// All 14 production services (no placeholders)
const services = [
    { name: 'api-gateway', image: 'gcr.io/prod-project/api-gateway:v1.2.3', envVars: { PORT: '8080' } },
    { name: 'user-service', image: 'gcr.io/prod-project/user-service:v2.1.0', envVars: { DB_HOST: 'prod-db' } },
    { name: 'payment-service', image: 'gcr.io/prod-project/payment-service:v3.0.1', envVars: { STRIPE_KEY: 'sk_live_123' } },
    { name: 'notification-service', image: 'gcr.io/prod-project/notification-service:v1.0.2', envVars: { SMTP_HOST: 'smtp.sendgrid.net' } },
    { name: 'search-service', image: 'gcr.io/prod-project/search-service:v2.3.0', envVars: { ELASTIC_HOST: 'prod-es' } },
    { name: 'auth-service', image: 'gcr.io/prod-project/auth-service:v1.5.0', envVars: { JWT_SECRET: 'secret' } },
    { name: 'cart-service', image: 'gcr.io/prod-project/cart-service:v2.0.1', envVars: { REDIS_HOST: 'prod-redis' } },
    { name: 'order-service', image: 'gcr.io/prod-project/order-service:v3.1.0', envVars: { DB_HOST: 'prod-db' } },
    { name: 'inventory-service', image: 'gcr.io/prod-project/inventory-service:v1.2.0', envVars: { DB_HOST: 'prod-db' } },
    { name: 'shipping-service', image: 'gcr.io/prod-project/shipping-service:v2.0.0', envVars: { SHIPPO_KEY: 'shippo_live_123' } },
    { name: 'review-service', image: 'gcr.io/prod-project/review-service:v1.1.0', envVars: { DB_HOST: 'prod-db' } },
    { name: 'recommendation-service', image: 'gcr.io/prod-project/recommendation-service:v2.2.0', envVars: { REDIS_HOST: 'prod-redis' } },
    { name: 'analytics-service', image: 'gcr.io/prod-project/analytics-service:v1.0.0', envVars: { BIGQUERY_DATASET: 'prod_analytics' } },
    { name: 'admin-service', image: 'gcr.io/prod-project/admin-service:v1.3.0', envVars: { ADMIN_SECRET: 'admin_secret' } },
];

// Deploy all services
for (const svc of services) {
    await createCloudRunService(svc.name, svc.image, svc.envVars);
}

// Export service URLs
export const serviceUrls = services.map(svc => 
    pulumi.interpolate`https://${svc.name}-svc-1234-uc.a.run.app`
);

Code Example 2: Bug Reproduction with Pulumi Automation API

This script reproduces the deletion bug using Pulumi’s Automation API. It deploys a test service with dot-containing labels, then re-deploys to trigger the regression.

// Reproduction script for Pulumi 3.100 GCP Cloud Run deletion bug
// Requires: Pulumi CLI 3.100.0, pulumi-gcp 7.21.0, GCP credentials

import * as pulumi from '@pulumi/pulumi';
import * as gcp from '@pulumi/gcp';
import { LocalWorkspace, Stack } from '@pulumi/pulumi/automation';

const projectId = 'pulumi-bug-repro';
const region = 'us-central1';
const stackName = 'dev';

async function runReproduction(): Promise {
    try {
        // Create workspace with vulnerable versions
        const workspace = await LocalWorkspace.create({
            projectSettings: {
                name: 'cloud-run-bug-repro',
                runtime: 'nodejs',
                backend: { url: 'file://~/.pulumi-local' },
            },
            pulumiVersion: '3.100.0',
            secretsProvider: 'passphrase',
            envVars: {
                PULUMI_CONFIG_PASSPHRASE: 'repro-passphrase',
                GCP_PROJECT: projectId,
                GCP_REGION: region,
            },
        });

        // Create or select dev stack
        const stack = await Stack.createOrSelect(stackName, workspace);

        // Define program with dot-containing labels
        const program = async () => {
            const service = new gcp.cloudrun.Service('repro-service', {
                name: 'bug-repro-svc',
                location: region,
                project: projectId,
                template: {
                    spec: {
                        containers: [{
                            image: 'gcr.io/google-samples/hello-app:1.0',
                            resources: { limits: { cpu: '1', memory: '512Mi' } },
                        }],
                    },
                    metadata: {
                        labels: {
                            'team.dev': 'true',
                            'env.dev': 'true',
                        },
                    },
                },
                metadata: {
                    labels: {
                        'team.dev': 'true',
                        'env.dev': 'true',
                    },
                },
                traffics: [{ percent: 100, latestRevision: true }],
            });

            return { serviceName: service.name };
        };

        // Configure stack
        await stack.setAllConfig({
            'gcp:project': { value: projectId },
            'gcp:region': { value: region },
        });
        workspace.program = program;

        // First deployment (succeeds)
        console.log('Deploying vulnerable service...');
        const upResult = await stack.up({ onOutput: console.log });
        console.log(`Deployment succeeded: ${upResult.outputs.serviceName.value}`);

        // Second deployment (triggers bug)
        console.log('Re-deploying to trigger deletion bug...');
        await stack.up({ onOutput: console.log });

        // Verify service deletion
        const services = await gcp.cloudrun.getServices({
            location: region,
            project: projectId,
        });
        const reproService = services.services.find(s => s.name === 'bug-repro-svc');
        if (!reproService) {
            console.error('BUG REPRODUCED: Cloud Run service was deleted unexpectedly!');
        } else {
            console.log('Service still exists, bug not reproduced.');
        }

        // Cleanup
        await stack.destroy({ onOutput: console.log });
        await workspace.removeStack(stackName);
    } catch (error) {
        console.error(`Reproduction failed: ${error.message}`);
        throw error;
    }
}

runReproduction().catch(console.error);

Code Example 3: Post-Incident Fix With Version Pinning

This code implements version pinning, label validation, and pre-deploy checks to prevent recurrence. It uses Pulumi Policy as Code to reject vulnerable provider versions.

// Post-incident fix: Version pinning and validation
// Pulumi CLI 3.99.2, pulumi-gcp 7.20.1

import * as pulumi from '@pulumi/pulumi';
import * as gcp from '@pulumi/gcp';
import { PolicyPack, validateResourceOfType } from '@pulumi/policy';

// Pinned common labels (dots replaced with underscores)
const safeLabels = {
    'team_platform': 'backend',
    'env_prod': 'true',
    'cost_center': '1234',
    'service_version': '1.2.3',
};

// Policy pack to reject vulnerable versions and invalid labels
new PolicyPack('version-policy-pack', {
    policies: [
        {
            name: 'pulumi-gcp-version-check',
            description: 'Reject pulumi-gcp 7.21.x to avoid deletion bug',
            validateResource: validateResourceOfType(gcp.Provider, (provider, args, reportViolation) => {
                const version = provider.version;
                if (version && (version >= '7.21.0' && version < '7.22.0')) {
                    reportViolation(
                        `pulumi-gcp version ${version} is vulnerable. Use 7.20.1 or 7.22.0+.`
                    );
                }
            }),
        },
        {
            name: 'cloud-run-label-validation',
            description: 'Reject labels with dots (temporary workaround)',
            validateResource: validateResourceOfType(gcp.cloudrun.Service, (service, args, reportViolation) => {
                const labels = service.metadata?.labels || {};
                const dotLabels = Object.keys(labels).filter(k => k.includes('.'));
                if (dotLabels.length > 0) {
                    reportViolation(
                        `Service ${args.name} has dot-containing labels: ${dotLabels.join(', ')}`
                    );
                }
            }),
        },
    ],
});

// Safe service creation function
async function createSafeCloudRunService(
    name: string,
    image: string,
    envVars: Record
): Promise {
    try {
        const service = new gcp.cloudrun.Service(name, {
            name: `${name}-svc`,
            location: region,
            project: projectId,
            template: {
                spec: {
                    containers: [{
                        image: image,
                        envs: Object.entries(envVars).map(([k, v]) => ({ name: k, value: v })),
                        resources: { limits: { cpu: '2', memory: '4Gi' } },
                    }],
                    serviceAccountName: serviceAccountEmail,
                },
                metadata: { labels: safeLabels },
            },
            metadata: { labels: safeLabels },
            traffics: [{ percent: 100, latestRevision: true }],
        });

        new gcp.cloudrun.IamMember(`${name}-public-access`, {
            service: service.name,
            location: region,
            project: projectId,
            role: 'roles/run.invoker',
            member: 'allUsers',
        });

        return service;
    } catch (error) {
        pulumi.log.error(`Failed to create safe service ${name}: ${error.message}`);
        throw error;
    }
}

// Pre-deploy version check
async function preDeployCheck(): Promise {
    const pulumiVersion = await getPulumiCliVersion();
    const gcpVersion = await getPulumiPluginVersion('gcp');
    console.log(`Pre-deploy: Pulumi ${pulumiVersion}, pulumi-gcp ${gcpVersion}`);

    if (pulumiVersion === '3.100.0') {
        throw new Error('Pulumi 3.100.0 is vulnerable. Aborting deployment.');
    }
    if (gcpVersion >= '7.21.0' && gcpVersion < '7.22.0') {
        throw new Error(`pulumi-gcp ${gcpVersion} is vulnerable. Aborting deployment.`);
    }
}

// Helper functions (valid implementations)
async function getPulumiCliVersion(): Promise {
    const { execSync } = require('child_process');
    return execSync('pulumi version').toString().trim();
}

async function getPulumiPluginVersion(plugin: string): Promise {
    const { execSync } = require('child_process');
    const output = execSync(`pulumi plugin ls --json`).toString();
    const plugins = JSON.parse(output);
    const target = plugins.find(p => p.name === plugin);
    return target?.version || '0.0.0';
}

// Run pre-deploy check before deployment
await preDeployCheck();

// Deploy all services with safe labels
for (const svc of services) {
    await createSafeCloudRunService(svc.name, svc.image, svc.envVars);
}

Performance Comparison: Pulumi Versions

We benchmarked Cloud Run deployment performance across three Pulumi versions to quantify the regression impact.

Metric

Pulumi 3.99.2 + pulumi-gcp 7.20.1

Pulumi 3.100.0 + pulumi-gcp 7.21.0

Pulumi 3.100.1 + pulumi-gcp 7.22.0

Deployment time per service

42s

38s

41s

False deletion rate (dot labels)

0%

100%

0%

Label character support

Dots allowed

Dots cause deletion

Dots allowed

Cloud Run integration tests

142

148 (no dot label tests)

163 (15 new dot tests)

Incident rate (per 1000 deployments)

0

14

0

Case Study: Fintech Startup Incident Response

• Team size: 6 infrastructure engineers, 12 backend engineers
• Stack & Versions: Pulumi CLI 3.100.0, pulumi-gcp 7.21.0, GCP Cloud Run, TypeScript 5.3, GitHub Actions, Datadog
• Problem: p99 API latency was 120ms, but after upgrading Pulumi to 3.100.0 for new IAM features, 14 production Cloud Run services were deleted during a routine deployment, causing 2.1M daily active users to lose access for 4 hours, with $47k in lost transaction revenue.
• Solution & Implementation: Rolled back Pulumi CLI to 3.99.2 in 15 minutes via GitHub Actions cache, pinned all Pulumi and provider versions in Pulumi.yaml files, implemented Pulumi Policy as Code to reject vulnerable provider versions, replaced dot-containing labels with underscores across all 14 services, added pre-deploy checks to GitHub Actions workflows to validate Pulumi versions before deployment.
• Outcome: 0 unplanned service deletions in 6 months post-fix, deployment time returned to 42s per service, saved $47k per potential incident, reduced incident response time for IaC issues from 4 hours to 12 minutes, 120 engineering hours saved per quarter from reduced fire drills.

Developer Tips

Tip 1: Explicitly Pin All IaC Tool and Provider Versions

For 15 years of writing IaC, the single biggest mistake I see teams make is leaving tool versions unpinned. When we upgraded to Pulumi 3.100.0, we didn’t pin the pulumi-gcp provider version, so it automatically pulled 7.21.0 which contained the regression. Unlike application dependencies, IaC tool regressions can delete production resources in seconds. You must pin every version: the Pulumi CLI, all provider plugins, and even runtime dependencies like Node.js or Python. Use infrastructure as code for your IaC tooling: define versions in Pulumi.yaml, then enforce them with Renovate or Dependabot to only allow patch updates after staging validation. For Pulumi, this means adding a plugins section to Pulumi.yaml with exact versions, not semver ranges. We now use a centralized Pulumi version config repo that all teams inherit from, which reduced version-related incidents by 92% in 6 months. Tool names to use: Pulumi, Renovate, Dependabot, GitHub Actions.

# Pulumi.yaml version pinning example
name: prod-cloud-run
runtime: nodejs
pulumiVersion: 3.99.2 # Exact CLI version, no ranges
plugins:
  - name: gcp
    version: 7.20.1 # Exact provider version
  - name: kubernetes
    version: 4.10.0

Tip 2: Add Pre-Deployment Validation for Resource Configuration

Even with pinned versions, you need to validate that your resource configuration is compatible with the provider version you’re using. In our case, the bug was triggered by labels with dots, which are valid in GCP Cloud Run but broke the Pulumi provider. We now run three layers of pre-deployment validation: first, a Pulumi Policy as Code check that rejects vulnerable provider versions and invalid label patterns. Second, a pulumi preview run in a staging stack that mirrors production exactly, with alerts if any delete operations are planned. Third, a direct API check against GCP to verify that existing resources match the Pulumi state before deployment. This would have caught the 3.100 bug immediately: the pulumi preview would have shown 14 delete operations for our Cloud Run services, which our staging policy would have rejected. Use tools like OPA (Open Policy Agent) to write reusable policies across all your IaC stacks, and integrate validation into your CI/CD pipeline so deployments fail fast if invalid operations are detected. We reduced false positive delete alerts by 78% after adding these checks. Tool names: Pulumi Policy as Code, OPA, GCP Cloud Run API, GitHub Actions.

# Pulumi Policy to reject delete operations for production services
new PolicyPack('prod-policy-pack', {
  policies: [
    {
      name: 'no-cloud-run-deletion',
      description: 'Reject any delete operations for production Cloud Run services',
      validateResource: validateResourceOfType(gcp.cloudrun.Service, (svc, args, report) => {
        if (args.type === 'delete') {
          report(`Cannot delete production Cloud Run service ${args.name}`);
        }
      }),
    },
  ],
});

Tip 3: Implement Canary Deployments for All IaC Changes

Mass deletion bugs like the Pulumi 3.100 incident are catastrophic because they affect all resources at once. Canary deployments for IaC solve this by rolling out changes to a small subset of resources first, validating them, then rolling out to the rest. For our Cloud Run services, we now use Pulumi stacks to create a canary stack that deploys 1 service with the new configuration, then uses GCP Cloud Run traffic splitting to send 5% of traffic to the canary. We monitor error rates, latency, and deletion events for 30 minutes before rolling out to the remaining 13 services. This would have limited the Pulumi 3.100 bug to 1 deleted service instead of 14, reducing outage time from 4 hours to 15 minutes. Canary deployments add 10-15 minutes to deployment time, but that’s negligible compared to the hours lost to outages. Use tools like Pulumi’s stack references to share configuration between canary and production stacks, and Datadog or Prometheus to monitor canary health. We’ve adopted this for all GCP, AWS, and Kubernetes resources, and haven’t had a mass outage since. Tool names: Pulumi, GCP Cloud Run, Datadog, Prometheus.

# Pulumi traffic splitting for canary deployment
new gcp.cloudrun.ServiceTraffic('canary-traffic', {
  service: prodService.name,
  location: region,
  project: projectId,
  traffics: [
    { percent: 95, revisionName: prodService.latestReadyRevisionName },
    { percent: 5, revisionName: canaryService.latestReadyRevisionName },
  ],
});

Join the Discussion

We’ve shared every detail of this incident, from the exact code that triggered it to the fixes that prevented recurrence. IaC regressions are a growing risk as providers add more features, and we want to hear from the community about how you’re handling this.

Discussion Questions

• Will Pulumi’s new provider testing framework eliminate regression risks for GCP resources by 2025?
• Is the productivity gain of unpinned IaC provider versions worth the risk of untested regressions?
• How does Terraform’s provider versioning model compare to Pulumi’s in preventing mass deletion bugs?

Frequently Asked Questions

What exactly caused the Pulumi 3.100 Cloud Run deletion bug?

The regression was introduced in pulumi-gcp v7.21.0, which normalized resource labels by replacing dots (.) with underscores (_) when computing the provider’s internal resource state. This caused Pulumi to see existing Cloud Run services with dot-containing labels as "new" resources, triggering a delete-recreate cycle. The recreate logic had an unhandled error that deleted the existing service before failing to create the new one, resulting in total service deletion. The bug was fixed in pulumi-gcp v7.22.0, shipped with Pulumi CLI 3.100.1.

How can I check if my Pulumi stacks are vulnerable to this bug?

First, check your installed pulumi-gcp version by running pulumi plugin ls. If the version is 7.21.x, you are vulnerable. Second, check your Cloud Run service definitions for labels containing dots (e.g., "team.platform": "backend"). Third, run pulumi preview with the vulnerable version: if it shows delete operations for existing Cloud Run services, you are at risk. We recommend immediately pinning pulumi-gcp to 7.20.1 or 7.22.0+ and replacing dot-containing labels with underscores.

Did Pulumi compensate affected users for the incident?

Pulumi acknowledged the regression 2 hours after our incident report, shipped a hotfix in Pulumi 3.100.1 within 8 hours, and issued $50k in Pulumi credits to all teams that reported service deletions. They also added 15 new integration tests for GCP Cloud Run label handling, contributed by both Pulumi engineers and open-source maintainers, and published a postmortem on their blog. We’ve since contributed our label validation policy to the pulumi/examples repo.

Conclusion & Call to Action

After 15 years of building and breaking production systems, I can say with certainty that IaC regressions are not a matter of if, but when. The Pulumi 3.100 bug deleted 14 production services in minutes, but it was entirely preventable with version pinning, pre-deployment validation, and canary rollouts. My opinionated recommendation: treat IaC tooling with the same rigor as production application code. Pin every version, test every provider upgrade in staging, and never roll out changes to all resources at once. The open-source community has made huge strides in IaC safety, but it’s up to teams to implement these practices. If you’re using Pulumi for GCP Cloud Run, audit your labels today, pin your versions, and share your learnings with the community.

100% False deletion rate for GCP Cloud Run services with dot-containing labels in Pulumi 3.100