AI PENTEST FOR CLOUD INFRASTRUCTURE: AWS, GCP & AZURE SECURITY TESTING | VIBEEVAL

What an AI pentest for cloud infrastructure actually covers

A cloud pentest is not a port scan plus a CVE database. It is an audit of the configuration that turns a cloud account into a system: IAM, networking, storage permissions, encryption, audit logging, and the runtime workloads sitting on top. AI pentest agents walk the cloud layer the same way attackers do — find a weakness, see what it is connected to, and chain.

Cloud misconfigurations cause 80% of breaches

A single exposed S3 bucket or overly permissive IAM role can compromise your entire infrastructure. Cloud environments are complex, and AI pentest agents systematically check every configuration that humans routinely miss. Across the cloud accounts we audit, the bugs cluster into a small set of patterns that recur across every provider.

Cloud infrastructure pentest checklist

1. Audit IAM configurations. Review roles, policies, and permissions for over-permissive access and privilege escalation paths.
2. Scan for open storage buckets. Check S3, GCS, and Blob Storage for public access and misconfigured ACLs.
3. Test serverless function security. Audit Lambda, Cloud Functions, and Azure Functions for injection, excessive permissions, and insecure triggers.
4. Verify network segmentation. Confirm VPCs, subnets, and network policies isolate production, staging, and internal services.
5. Check for exposed metadata endpoints. Test for SSRF that can reach cloud metadata and steal instance credentials.
6. Audit database access controls. Verify databases are not publicly accessible and require authentication and encryption.
7. Test container security. Scan container images, check for privileged containers, verify pod security policies.
8. Verify encryption at rest and in transit. Confirm encryption on every data store and TLS on every channel.
9. Scan for misconfigured security groups. Check firewall rules for over-permissive inbound and outbound access.
10. Test cloud API permissions. Verify cloud management APIs are secured and cannot be abused for lateral movement.

Benefits of AI pentest for cloud

Covers AWS, GCP, and Azure

AI pentest agents understand the security models of all major cloud providers and test provider-specific vulnerabilities.

Finds misconfigurations humans miss

Systematically audits hundreds of configuration settings that are easy to overlook during manual review.

Tests serverless and container workloads

Goes beyond traditional infrastructure testing to cover Lambda, ECS, Kubernetes, and serverless architectures.

Generates compliance-ready reports

Produces detailed findings mapped to SOC 2, ISO 27001, and CIS Benchmarks for audit and compliance needs. See Compliance Penetration Testing.

Pentest by cloud provider

The same six classes of misconfiguration recur across AWS, GCP, and Azure. The names and resource shapes change.

AWS deep dive

The largest fleet, the largest blast radius, and the most well-known patterns.

• S3. Block Public Access at the account level, no s3:* on resource policies, no aws:PrincipalOrgID missing on cross-account buckets. The agent attempts anonymous list, anonymous get, and anonymous put on every discovered bucket.
• IAM. No * on Action or Resource outside narrow break-glass roles. The agent looks for iam:PassRole plus lambda:UpdateFunctionConfiguration as a classic privilege escalation pair, and any role whose trust policy allows sts:AssumeRole from a wildcard principal.
• Lambda. Function role least privilege, no environment variables holding plaintext secrets, no public function URL without auth.
• API Gateway. Authorizers attached to every method, not just the root. CORS not wildcard.
• Metadata SSRF. IMDSv2 enforced (HttpTokens: required), hop limit set to 1. The agent probes web apps reachable from the internet for SSRF and confirms metadata is unreachable.
• KMS. Keys not granted to overly broad principals; key policies do not allow kms:* from *.
• RDS. Not publicly accessible, encryption at rest enabled, automatic backups on, deletion protection on for production.

GCP deep dive

GCP’s IAM model is binding-based and easier to reason about, but the same misconfigurations recur.

• Cloud Storage. No allUsers or allAuthenticatedUsers reader binding on private buckets. The agent attempts anonymous list and get against every discovered bucket. Uniform bucket-level access enabled.
• IAM. No primitive roles (Owner, Editor, Viewer) on production projects. Service account keys rotated; service accounts not attached to compute instances unless required. No iam.serviceAccounts.actAs on broad sets of users.
• Cloud Functions. Allow-unauthenticated only on functions that should be public; otherwise IAM-gated. VPC connector configured for outbound traffic to private resources.
• Firestore. Security rules audited (see Firebase Scanner and Backend Security: Firebase Rules). The default if true rule is the single most common finding on AI-generated apps.
• Metadata SSRF. Metadata server requires the Metadata-Flavor: Google header on most paths but the legacy v1beta1 endpoint does not — confirm it is disabled.

Azure deep dive

Azure’s identity model spans Entra ID, RBAC, and Managed Identity. The pentest has to walk all three.

• Blob Storage. Public access disabled at the account level, no anonymous read on containers, shared access signatures (SAS) scoped narrowly with short expiry.
• RBAC. No Owner or Contributor at the subscription scope for service principals. Custom roles preferred over built-in for least privilege.
• Managed Identity. Not over-scoped; identities attached to compute do not have Owner on the resource group.
• Azure Functions. Function-level auth enabled for non-public endpoints; VNet integration on internal functions.
• Cosmos DB. Firewall rules in place; no public network access unless required; managed identity preferred over keys.
• Network Security Groups. No 0.0.0.0/0 ingress on RDP, SSH, or database ports.

Top cloud misconfigurations AI finds

Public S3 / GCS / Blob buckets

AI scans for publicly accessible storage buckets containing backups, logs, user data, and configuration files. In 2024, exposed S3 buckets caused 12% of all reported data breaches.

Overly permissive IAM roles

AI audits IAM policies for wildcards, excessive permissions, and roles that violate least-privilege principles. A single over-permissioned role can give attackers access to your entire AWS account.

Exposed metadata endpoints

AI tests whether cloud instance metadata is accessible through SSRF in web applications. Exposed metadata can leak IAM credentials, API keys, and instance configuration.

Misconfigured security groups

AI scans for security groups with 0.0.0.0/0 ingress rules on sensitive ports (SSH, RDP, database ports). Open security groups are a top entry point for cloud-based attacks.

Unencrypted data at rest

AI checks whether databases, storage buckets, and EBS volumes have encryption enabled. Unencrypted data at rest violates every major compliance framework.

Missing CloudTrail / audit logging

AI verifies that audit logging is enabled across all services. Without logging, you can’t detect or investigate security incidents.

Anonymized real-world findings

Examples from cloud accounts we have audited. Account IDs and resource names are altered.

World-readable S3 bucket

Resource: s3://<company>-prod-backups. Evidence: anonymous s3:GetObject returned a SQL dump from the previous night. Block Public Access was disabled at the account level and the bucket policy allowed Principal: *. Impact: full database export to anyone with the URL. Fix: enable Block Public Access account-wide, remove the public principal, rotate any credentials present in the dump.

Over-privileged Lambda role

Resource: arn:aws:iam::<account>:role/lambda-data-sync. Evidence: the role had AdministratorAccess attached. The Lambda was triggered from API Gateway with no auth. Impact: SSRF or input-injection on the Lambda equaled full account compromise. Fix: scope the role to only the resources the function needs (s3:GetObject on one bucket, dynamodb:PutItem on one table). Add an authorizer to the API Gateway method.

IMDSv1 enabled allowing SSRF

Resource: an EC2 fleet behind an internet-facing ALB. Evidence: the web app had a webhook-test endpoint that fetched arbitrary URLs server-side. The agent submitted http://169.254.169.254/latest/meta-data/iam/security-credentials/ and got back the role’s temporary credentials. Impact: attacker pivots from web app SSRF to the IAM role. Fix: enforce IMDSv2 on the launch template (HttpTokens: required), set hop limit to 1, and add an outbound allowlist to the SSRF-prone endpoint.

GCS bucket with allUsers reader

Resource: gs://<company>-userfiles. Evidence: the bucket binding included roles/storage.objectViewer for allUsers. The bucket contained user-uploaded ID documents. Impact: full leak of user uploads. Fix: remove the allUsers binding, enable uniform bucket-level access, and audit access logs for the period the binding was live.

Exposed Firestore (no security rules)

Resource: a Firestore database in a Firebase project. Evidence: rules were match /{document=**} { allow read, write: if true; }. Impact: any client could read or write any document including private user records. Fix: write per-collection rules that check request.auth.uid matches the document owner. See Firebase Scanner and AI Pentest for SaaS.

Public RDS instance

Resource: db.<company>.us-east-1.rds.amazonaws.com. Evidence: the RDS instance had PubliclyAccessible: true and a security group with 0.0.0.0/0 on port 5432. Impact: any attacker who guessed or stole credentials had a direct path to the production database. Fix: set PubliclyAccessible: false, restrict the SG to the application VPC, rotate credentials.

Serverless security testing

Serverless architectures (AWS Lambda, Google Cloud Functions, Azure Functions, Vercel Edge Functions) introduce unique security challenges. Functions often run with overly broad IAM permissions because developers grant full access during development and never tighten it. AI pentest agents test function permissions by attempting to access resources outside the function’s intended scope.

Event-driven architectures add another attack surface. AI tests whether event sources (API Gateway, S3 triggers, SQS queues) properly validate input before invoking functions. A malicious S3 object name or SQS message can inject commands into poorly written Lambda handlers. AI also tests for cold-start timing attacks and function-level DoS through concurrent invocation floods.

The standard finding pattern is: a function whose role can read every secret in Secrets Manager because the developer used secretsmanager:* on Resource: * to make the demo work, then never tightened it.

Containers and Kubernetes

Container workloads add their own surface. The pentest covers image, runtime, and orchestration.

• Image scanning. CVE scan of every layer; check for latest tags pinned to nothing; check for secrets baked into images.
• Runtime config. No privileged containers; no hostNetwork: true or hostPID: true outside narrow exceptions; resource limits set.
• RBAC. No cluster-admin bindings outside the cluster operators. Service accounts scoped to namespaces.
• Network policies. Default-deny ingress and egress; explicit allows only.
• Pod security. Run as non-root, read-only root filesystem, drop all Linux capabilities, seccomp profile applied.
• Secrets. Mounted from a secret manager (External Secrets, CSI driver) rather than in-line Secret objects.

Terraform / CDK / CloudFormation as input artifacts

The cheapest cloud pentest reads the infrastructure-as-code before the resources are deployed. The agent ingests Terraform plans, CDK synthesized templates, and CloudFormation YAML, and flags:

• IAM policies with wildcard Action or Resource
• S3 buckets without Block Public Access
• EC2 launch templates with IMDSv1
• RDS instances with publicly_accessible = true
• Security groups with 0.0.0.0/0 on sensitive ports
• Resources missing encryption at rest
• Lambdas with environment variables that look like secrets

This catches the bug before it costs anything.

Fix prompts you can paste

Lock down a public S3 bucket

The bucket <bucket> is currently world-readable. Update the Terraform resource to enable Block Public Access at both the bucket and account level. Remove any bucket policy with Principal: *. Add a CI check using tflint or checkov that fails the build if any future bucket lacks Block Public Access.

Fix an over-privileged Lambda role

The Lambda <function> has AdministratorAccess. Replace it with a least-privilege policy that grants only the actions and resources the function uses: <list>. Add a deny statement for iam:* and kms:* outside the specific KMS key the function needs. Document the policy in the function’s README.

Enforce IMDSv2 on all EC2 instances

Update the launch template and all running instances to require IMDSv2: set HttpTokens: required and HttpPutResponseHopLimit: 1. For new instances, add this to the Terraform module so future fleets inherit the setting. Add a Config rule (ec2-imdsv2-check) to alert on any instance still allowing IMDSv1.

Related guides

• AI Pentest for APIs — API pentest including SSRF leading to metadata
• AI Pentest for Web Applications — web pentest that often surfaces the SSRF
• AI Pentest for SaaS — multi-tenant cloud isolation
• Compliance Penetration Testing — SOC 2, ISO 27001, PCI-DSS reports
• AI Penetration Testing Guide — full methodology
• Firebase Scanner — Firestore Security Rules audit
• Backend Security: Firebase Rules — deep dive on rules
• Supabase RLS Checker — verify every Supabase table has a policy
• Token Leak Checker — find exposed cloud keys in client bundles
• Safety: Supabase — Supabase-specific risks
• Safety: Firebase — Firebase-specific risks
• VibeEval vs Snyk — SAST + SCA vs runtime cloud pentest

Pentest your cloud infrastructure today

VibeEval’s AI pentest agents audit your cloud configuration across AWS, GCP, and Azure. Find misconfigurations, exposed resources, and privilege escalation paths automatically.