# Hands-On Attack-to-Defense Playbooks
> **Intro:** Reading attack patterns is useful, but engineering judgment gets stronger when the same scenario is viewed as an **attack path, a detection problem, a containment problem, and a hardening problem**. These playbooks are designed to help AppSec, DevSecOps, cloud, and Kubernetes engineers practice that full loop.
>
> **How to use this page:** pick one scenario, reproduce it safely in a lab, collect the evidence you would expect in a real environment, and finish only when you can explain both the **fix** and the **proof that the fix works**.
## What a good hands-on playbook contains
Each playbook should answer six questions:
1. **What is the attacker trying to achieve?**
2. **Which trust edge is being crossed?**
3. **What evidence would we see in code, config, logs, and cloud control planes?**
4. **How do we contain the issue without creating more damage?**
5. **What secure default or control pattern prevents recurrence?**
6. **How do we verify the environment is truly better, not just quieter?**
## Suggested playbook sequence
| Playbook | Best role fit | Primary skill you build |
| ---------------------------------------------------- | --------------------------------- | ------------------------------------------------------------ |
| Broken API authorization and tenant-boundary abuse | AppSec, backend security | abuse thinking, authorization review, test design |
| SSRF to metadata or internal-control-plane abuse | AppSec, cloud security | trust-boundary reasoning, egress control, identity isolation |
| CI token theft and pipeline-to-repository compromise | DevSecOps, platform security | runner trust, secret handling, blast-radius reduction |
| Kubernetes workload-to-cluster escalation | Kubernetes, cloud-native security | pod security, workload identity, containment |
| IAM / IaC privilege drift and cloud escalation | cloud security, DevSecOps | policy review, blast-radius analysis, detective guardrails |
| Dependency or build-provenance compromise | supply-chain security | artifact trust, attestations, rollback discipline |
## Playbook 1 — Broken API authorization and tenant-boundary abuse
### Attack path
A user can read or modify another tenant's object because the service validates authentication but not **object-level authorization**. The attacker iterates IDs, abuses predictable references, or pivots through an admin/helper endpoint.
### Practice safely
* Use a lab like Juice Shop or a deliberately vulnerable internal demo app.
* Create two users from different tenants.
* Capture the exact request and response differences when the wrong tenant's object is fetched or mutated.
* Add a regression test that fails before the fix and passes after the fix.
### Evidence to collect
* access logs showing subject, object, route, and decision outcome;
* application logs for authN success without matching authZ checks;
* unit/integration tests that prove cross-tenant access was previously allowed;
* design docs or route definitions that show the object-owner check was implicit instead of explicit.
### Defensive response
* enforce object-level authorization in the service layer, not only in the UI or gateway;
* make tenant context explicit in request handling and storage access;
* log denied and allowed sensitive object actions with consistent identifiers;
* add pre-release abuse tests for horizontal and vertical authorization.
### “Done” looks like
* the same abuse request is denied consistently across UI, API, and background jobs;
* logs now show why the action was denied;
* automated tests prevent the bug from returning;
* reviewers can point to the exact policy or code path enforcing ownership.
**Best cross-links:**
* [API Authorization, Business Flows, and Third-Party API Consumption](/architecture-api-crypto-and-identity/index/api-authorization-business-flows-and-third-party-api-consumption.md)
* [Business Logic Vulnerabilities and Verification](/application-security-and-secure-sdlc/index-1/business-logic-vulnerabilities-and-verification.md)
* [Worked Example — API Review Lab](https://github.com/D3One/Product-Security-Gitbook/blob/main/22-learning-paths-and-labs/worked-example-api-review-lab.md)
## Playbook 2 — SSRF to metadata or internal-control-plane abuse
### Attack path
A file-fetcher, webhook validator, PDF renderer, avatar importer, or URL-preview feature allows server-side requests to attacker-controlled destinations. The attacker then targets metadata services, private admin interfaces, or internal APIs.
### Practice safely
* Use a local SSRF-safe lab or a non-production sandbox.
* Reproduce the issue against a harmless local endpoint first.
* Test whether the component can reach link-local or RFC1918 ranges, internal DNS names, or cloud metadata addresses.
* Record what identity or credential would be exposed if the same pattern existed in production.
### Evidence to collect
* app logs for outbound fetch destinations;
* proxy, egress, or VPC flow logs;
* cloud audit logs if metadata-derived credentials are later used;
* code paths that accept URL input without scheme/host allow-listing.
### Defensive response
* deny metadata-service and private-range access unless explicitly required;
* put outbound proxy or egress filtering in front of fetcher-style components;
* use workload identity instead of static credentials, and scope it narrowly;
* normalize URL parsing and reject dangerous schemes or redirect chains.
### “Done” looks like
* the component can only call approved destinations;
* metadata access is blocked or meaningless because the attached identity is tightly scoped;
* detections exist for suspicious outbound fetch patterns;
* tests cover redirect, DNS rebinding, and internal-address edge cases.
**Best cross-links:**
* [SSRF, File Fetch, and Parser Abuse](/application-security-and-secure-sdlc/index-1/ssrf-file-fetch-and-parser-abuse.md)
* [Zero-Trust Egress and Private Connectivity Patterns](/architecture-api-crypto-and-identity/index-1/zero-trust-egress-and-private-connectivity-patterns.md)
* [Cloud Attack Chains](/attack-paths-testing-detection-and-hardening/index-1/cloud-attack-chains.md)
## Playbook 3 — CI token theft and pipeline-to-repository compromise
### Attack path
A pipeline leaks a long-lived token, over-privileged runner identity, or repository secret. The attacker modifies workflows, publishes malicious artifacts, accesses packages, or uses CI as a stepping stone into cloud resources.
### Practice safely
* Use a disposable training repo or pipeline sandbox.
* Inventory every secret, token, and cloud identity reachable from the runner.
* Simulate exfiltration by proving which actions the token could take, without actually performing harmful changes.
* Compare behavior between ephemeral and persistent runners.
### Evidence to collect
* pipeline logs and job definitions;
* runner configuration and network placement;
* audit trails for repo, package, registry, and cloud actions;
* evidence of branch protection, CODEOWNERS, approval gates, and environment protections.
### Defensive response
* replace broad static secrets with short-lived federation where possible;
* isolate runners by trust tier and keep privileged jobs off shared runners;
* lock down workflow modification paths and deployment approvals;
* generate provenance, attestations, and release evidence for high-trust builds.
### “Done” looks like
* token blast radius is small and easy to explain;
* runner compromise does not automatically imply repo-admin or cloud-admin access;
* critical workflow changes require controlled review;
* artifact trust can be traced through provenance and release evidence.
**Best cross-links:**
* [Runner Isolation and Trust Boundaries](/devsecops-cicd-and-supply-chain/index-1/runner-isolation-and-trust-boundaries.md)
* [GitHub Actions for Product Security](/devsecops-cicd-and-supply-chain/index-1/github-actions-for-product-security.md)
* [GitLab CI/CD Modern Security Patterns](/devsecops-cicd-and-supply-chain/index-1/gitlab-ci-cd-modern-security-patterns.md)
* [Signing, Attestation, and Verification — Legacy vs Current](/devsecops-cicd-and-supply-chain/index-1/signing-attestation-and-verification-legacy-vs-current.md)
## Playbook 4 — Kubernetes workload-to-cluster escalation
### Attack path
A container starts with unsafe privileges, a mounted service account token, weak admission controls, or excessive namespace permissions. The attacker gets code execution in the pod, then attempts credential theft, secret access, lateral movement, or cluster-control-plane actions.
### Practice safely
* Use Kubernetes Goat, EKS Goat, or a disposable sandbox cluster.
* Start from a workload with one intentionally weak control at a time: privileged mode, hostPath, broad RBAC, or unrestricted egress.
* Record which escalation steps are blocked and which remain possible.
* Compare namespace policy labels and admission decisions before and after hardening.
### Evidence to collect
* Kubernetes audit logs and admission decisions;
* workload YAML, RBAC bindings, and service-account references;
* runtime detections from Falco, Tetragon, or cloud-native signals;
* cloud audit logs if the workload federates into provider IAM.
### Defensive response
* apply Pod Security Standards or equivalent admission policy;
* minimize service-account privileges and avoid token mounting where unnecessary;
* enforce network policy, image provenance, and runtime signal collection;
* make namespace trust tiers explicit instead of letting everything behave the same.
### “Done” looks like
* the workload no longer escalates through the same route;
* the namespace has an explicit security posture;
* RBAC and workload identity are easy to explain;
* runtime detections are validated against a safe simulation.
**Best cross-links:**
* [Kubernetes Attack Chains](/attack-paths-testing-detection-and-hardening/index-1/kubernetes-attack-chains.md)
* [Kubernetes Hardening](/cloud-kubernetes-and-infrastructure-security/index-1/kubernetes-hardening.md)
* [Kubernetes API Access Hardening](/cloud-kubernetes-and-infrastructure-security/index-1/kubernetes-api-access-hardening.md)
* [Cloud and Kubernetes Runtime Investigation Playbooks](/attack-paths-testing-detection-and-hardening/index/cloud-and-kubernetes-runtime-investigation-playbooks-and-containment-templates.md)
## Playbook 5 — IAM / IaC privilege drift and cloud escalation
### Attack path
A small Terraform, ARM/Bicep, or CloudFormation change introduces broader trust than intended: wildcard actions, public exposure, cross-account role assumption, or logging gaps. The attacker chains those misconfigurations into privilege escalation or stealth.
### Practice safely
* Use Terragoat, CloudGoat, or a disposable sandbox account/subscription/project.
* Review the delta at both the IaC layer and the deployed-resource layer.
* Map which principals, networks, and data stores are newly exposed.
* Verify whether the same issue would be caught by review, policy-as-code, and detective controls.
### Evidence to collect
* pull request diff and plan/apply output;
* cloud IAM policy simulator results or equivalent reasoning;
* posture findings and audit logs;
* explicit list of resources whose blast radius changed.
### Defensive response
* require least-privilege review for non-human identities and trust policies;
* codify guardrails with policy-as-code and preventive controls;
* maintain immutable logging for privilege and trust-path changes;
* review public exposure and transitive trust, not only resource-local settings.
### “Done” looks like
* the risky change is blocked before apply or flagged immediately after;
* reviewers can name the principal, trust edge, and resource blast radius;
* logging exists for the risky control-plane actions;
* the secure pattern is captured as a reusable baseline.
**Best cross-links:**
* [Cloud Attack Chains](/attack-paths-testing-detection-and-hardening/index-1/cloud-attack-chains.md)
* [AWS IAM and Role Design](/cloud-kubernetes-and-infrastructure-security/index/aws-iam-and-role-design.md)
* [IaC and Policy as Code](https://github.com/D3One/Product-Security-Gitbook/blob/main/08-infrastructure-and-cloud-security/iac-and-policy-as-code.md)
* [Terraform Security Scanning and Checkov](/cloud-kubernetes-and-infrastructure-security/index/terraform-security-scanning-and-checkov.md)
## Playbook 6 — Dependency or provenance compromise
### Attack path
A malicious dependency update, build-script modification, compromised maintainer path, or unsigned artifact enters the pipeline. The attacker aims to get untrusted code built, published, or deployed under the cover of normal automation.
### Practice safely
* use a training repository or dependency-management demo project;
* compare a normal update flow with one that changes trust assumptions: maintainer, source, lockfile, or build step;
* verify whether provenance, attestation, and policy checks would detect the difference;
* test the rollback path, not only the prevention path.
### Evidence to collect
* dependency PR history and review approvals;
* SBOM, provenance, or attestation artifacts;
* package-manager lockfile changes and source redirects;
* release evidence linking commit, build, artifact, and deploy decision.
### Defensive response
* protect workflow files, release paths, and package publication identities;
* use automated dependency updates with policy and test controls;
* generate provenance and verify it before promotion;
* keep rollback and revocation playbooks ready for suspect releases.
### “Done” looks like
* high-trust releases can be traced back to reviewed source and controlled builds;
* suspicious dependency or workflow changes are visible quickly;
* deployment promotion depends on trust evidence, not only build success;
* rollback is rehearsed and not invented during the incident.
**Best cross-links:**
* [Software Supply Chain Foundations](/devsecops-cicd-and-supply-chain/index-1/software-supply-chain-foundations.md)
* [GitLab Release Evidence](/devsecops-cicd-and-supply-chain/index-1/gitlab-release-evidence.md)
* [Chainloop and Supply-Chain Evidence](/devsecops-cicd-and-supply-chain/index-1/chainloop-and-supply-chain-evidence.md)
* [OpenSSF Scorecard and Dependency Trust Signals](/learning-labs-interview-and-templates/index-2/product-security-tooling-landscape-and-inventory.md)
## A simple scoring model for practice
Use a 0–2 score for each area after you finish a playbook:
* **Exploit understanding** — can you explain the attacker’s path without hand-waving?
* **Evidence mapping** — can you name the logs, configs, and artifacts that prove it happened?
* **Containment quality** — can you reduce damage before the full fix lands?
* **Prevention quality** — did you implement a reusable control rather than a one-off patch?
* **Verification quality** — can you prove the same path no longer works?
A playbook is mature when the team can repeat the exercise with a new service or environment without needing the same expert every time.
## Suggested reference links
* MITRE ATT\&CK Containers Matrix —
* CISA / NSA Kubernetes Hardening Guidance —
* NIST SSDF —
* SLSA —
* OWASP ASVS —
\---*Author attribution: Ivan Piskunov, 2026 - Educational and defensive-engineering use.*
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