Hacking the Cloud: The Crowdsourced Encyclopedia That Red and Blue Teams Actually Use

Hook

Most cloud security vulnerabilities aren't zero-days—they're misconfigurations that have been publicly documented for years. The challenge isn't discovering new attacks; it's knowing where to find the ones that actually work.

Context

Cloud security moved faster than the documentation could keep up. While official AWS, Azure, and GCP documentation excelled at explaining how services work, they naturally didn't catalog the creative ways those same services could be exploited. Security researchers would publish brilliant attack techniques in blog posts that would fade into obscurity, their URLs rotting as domains expired or companies rebranded. Red teamers reinvented the same wheels, and blue teamers struggled to find defensive guidance that wasn't buried in vendor whitepapers or conference slides.

The problem wasn't a lack of knowledge—it was the lack of a canonical reference. Traditional security frameworks like MITRE ATT&CK provided excellent taxonomy but minimal tactical detail. Commercial training platforms offered hands-on labs but locked knowledge behind paywalls. What the cloud security community needed was something between Wikipedia's collaborative model and a practitioner's notebook: a living encyclopedia where offensive techniques and defensive countermeasures could be documented, credited to original researchers, and maintained as cloud platforms evolved. Hacking the Cloud emerged to fill that gap, creating a single source of truth for the techniques security professionals were already using but constantly having to rediscover.

Technical Insight

At its core, Hacking the Cloud is a static site generator deployment wrapped in a carefully designed content architecture. The repository uses Hugo as its static site generator, with content organized as markdown files following a taxonomy that mirrors how security professionals actually think about cloud attacks. Rather than organizing content by cloud service (which would be overwhelming given the hundreds of services across providers), the structure groups techniques by attack objective and stage in the kill chain.

The deployment architecture is elegantly simple: a Dockerfile that packages the Hugo build environment, coupled with GitHub Actions workflows that automatically build and deploy to GitHub Pages whenever content is merged to main. Here's the essence of the build process:

FROM klakegg/hugo:0.101.0-ext-alpine AS builder
WORKDIR /src
COPY . .
RUN hugo --minify

FROM nginx:alpine
COPY --from=builder /src/public /usr/share/nginx/html
EXPOSE 80
CMD ["nginx", "-g", "daemon off;"]

This multi-stage build keeps the final container lean—the Hugo toolchain is discarded after compilation, leaving only the static HTML, CSS, and JavaScript served by nginx. The entire site becomes a collection of pre-rendered pages that load instantly and can be hosted virtually anywhere. For contributors, this means zero infrastructure complexity: write markdown, submit a pull request, and the automation handles the rest.

What makes the content architecture particularly effective is its dual perspective structure. Each technique typically includes both the offensive "how to exploit this" narrative and the defensive "how to detect and prevent this" countermeasure. Take the classic AWS SSRF (Server-Side Request Forgery) attacks against metadata services. The offensive section might document:

# Exploit SSRF to retrieve IAM credentials from metadata service
curl http://169.254.169.254/latest/meta-data/iam/security-credentials/

# If IMDSv1 is enabled, this returns the role name
# Then retrieve temporary credentials:
curl http://169.254.169.254/latest/meta-data/iam/security-credentials/[ROLE-NAME]

# Returns JSON with AccessKeyId, SecretAccessKey, and Token
# Configure AWS CLI with these temporary credentials:
export AWS_ACCESS_KEY_ID="ASIA..."
export AWS_SECRET_ACCESS_KEY="..."
export AWS_SESSION_TOKEN="..."

aws sts get-caller-identity

The defensive counterpart then explains IMDSv2 (Instance Metadata Service Version 2) hardening, requiring a PUT request to obtain a session token before metadata access:

# IMDSv2 requires a token obtained via PUT request
TOKEN=`curl -X PUT "http://169.254.169.254/latest/api/token" -H "X-aws-ec2-metadata-token-ttl-seconds: 21600"`

# Then use the token in subsequent requests
curl -H "X-aws-ec2-metadata-token: $TOKEN" http://169.254.169.254/latest/meta-data/

# SSRF exploits typically can't perform PUT requests, mitigating the attack

This paired approach makes the resource valuable for both penetration testers learning attack vectors and security engineers hardening environments. The markdown-based content model also enables version control of security knowledge—you can see when techniques were added, who contributed them, and how defenses evolved as cloud providers patched vulnerabilities.

The contribution model deliberately lowers barriers to entry. Unlike academic security journals or vendor documentation, the review process focuses on practical accuracy rather than formal prose. Contributors are encouraged to credit original researchers, link to source materials, and provide working code examples. This creates a knowledge graph where readers can trace techniques back to original research while benefiting from the community's collective refinement of those techniques into reproducible steps.

The site's search and navigation leverage Hugo's taxonomy features to enable multiple discovery paths. You can browse by cloud provider (AWS, Azure, GCP), by technique category (reconnaissance, privilege escalation, persistence), or by affected service (S3, Lambda, Azure AD). This multi-dimensional organization acknowledges that different users approach the same content from different mental models—a red teamer preparing for an AWS engagement versus a blue teamer investigating suspicious Lambda activity need different entry points to the same underlying knowledge.

Gotcha

The project's greatest strength—community curation—is also its most significant limitation. Content coverage is wildly uneven, with AWS techniques receiving the lion's share of documentation while Azure and GCP sections remain relatively sparse. This reflects the community's experience and engagement rather than the relative security posture of the platforms. If you're securing a GCP-heavy environment, you'll find yourself constantly pivoting to scattered blog posts and vendor documentation because the centralized resource simply doesn't have the depth yet.

Content freshness is another persistent challenge. Cloud providers continuously patch security issues, deprecate services, and change authentication mechanisms. A technique documented as working in 2021 might fail silently in 2024 because AWS changed IAM policy evaluation logic or Azure modified how managed identities handle token requests. The repository has no systematic verification process to validate that documented techniques still work. Contributors update content when they discover drift, but there's no automated testing infrastructure spinning up cloud resources to verify each attack chain still functions. This means you'll occasionally find yourself debugging why a documented technique fails, unsure if you're implementing it incorrectly or if the cloud provider patched the underlying vulnerability months ago.

The open contribution model also means technical depth varies dramatically between entries. Some techniques include meticulously documented code examples, detection queries for SIEM platforms, and links to defensive tooling. Others are skeletal outlines that assume significant prior knowledge, essentially reading as "there's an SSRF here" without elaborating on exploitation methodology or defensive controls. For teams using this as a training resource, this inconsistency means you can't rely on it as a self-contained learning path—it works best as a supplementary reference for practitioners who already have foundational cloud security knowledge.

Verdict

Use if: You're a penetration tester, red teamer, or security researcher who needs a quick reference for cloud attack techniques and wants to avoid reinventing documented exploits. Use it if you're a security engineer building defensive controls and want to understand the offensive techniques you're defending against—the paired offensive/defensive structure makes it excellent for threat modeling. Use it if you're preparing for cloud security certifications like OSCP, PNPT, or vendor-specific security credentials and need practical attack scenarios beyond what official study guides provide. Use it if you're contributing to the security community's knowledge base and want a low-friction way to document techniques you've discovered or refined. Skip if: You need guaranteed up-to-date, vendor-validated security guidance for production environments—stick with official cloud provider security documentation and CIS benchmarks for compliance requirements. Skip it if you're completely new to cloud security and need structured learning paths with hands-on labs—platforms like CloudGoat or commercial training labs provide better scaffolding for beginners. Skip it if you need comprehensive Azure or GCP coverage comparable to the AWS content—the community simply hasn't built that depth yet, and you'll need to supplement heavily with other resources.