KubeXHunt: Kubernetes Post-Compromise Security Assessment

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What Is KubeXHunt?

KubeXHunt is an automated Kubernetes post-compromise assessment framework. Drop it onto a compromised pod and get:

• 27 continuous enumeration phases — from pod basics to cluster compromise, node escape, cloud pivoting, and supply chain attacks
• Automated attack chain generation — finds paths from your current pod to cluster-admin, complete with exploitation steps
• Zero external dependencies — runs on Python 3.9+ stdlib only; works on any Kubernetes cluster
• Multi-cloud support — detects and pivots through EKS IRSA, GKE Workload Identity, Azure Pod Identity
• Structured findings — MITRE ATT&CK for Containers, CWE, CIS Benchmark, and CVSS 3.1 severity mapping
• Multiple output formats — JSON, HTML, SARIF (for CI/CD), Markdown, and GraphViz attack graphs

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Credits to Chandrapal Badshah for providing exceptional training on Kubernetes Security, which significantly contributed to the development of this tool and deepened my understanding of Kubernetes security practices.

Special thanks to Payatu for sponsoring and providing access to this training, enabling the research and development behind KubeXHunt.

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Why Does KubeXHunt Exist?

Kubernetes security tools fall into two camps:

1. Posture scanners (kube-bench, kubescape, Trivy) — find misconfiguration before deployment
2. Network/RBAC analyzers (KubeHound) — map the entire cluster's attack surface from the API

But there's a gap: What happens when you already have code execution inside a pod?

Existing post-compromise frameworks (Peirates, kube-hunter, BOtB) are 3–5 years old and archived. KubeXHunt fills that void by:

• Automating real-world attack chains — not just finding individual vulnerabilities, but chaining them into exploitable path
• Providing in-cluster execution — no kubectl, no API access required; works from a compromised pod
• Generating actionable exploitation code — see the exact shell commands/YAML to move laterally and escalate
• Integrating cloud pivoting — because Kubernetes on EKS/GKE/AKS means compromising the cloud account is the endgame

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Quick Start

Installation

# Install via pip (Python 3.9+)
pip install kubexhunt

# Verify installation
kubexhunt --help

# Retrieve the report
kubectl logs kubexhunt-scan > report.json

As a Python Script

# Clone and install
git clone https://github.com/mr-xhunt/kubeX.git
cd kubexhunt
pip install -e ".[dev]"

# Run a full assessment
python3 -m kubexhunt --output report.html

# Run specific phases (read-only)
python3 -m kubexhunt --phase 1 2 3 --no-mutate

# Run in stealth mode
python3 -m kubexhunt --stealth 2 --fast --no-mutate

As a One-Liner

python3 kubexhunt.py --output report.json --diff previous.json

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Example: Pod → Cloud Account Compromise

# 1. Run KubeXHunt
kubexhunt --output report.json

# 2. Review findings
# - Finding: ServiceAccount has get/create pod permissions
# - Finding: IMDS endpoint reachable (EC2 metadata)
# - Finding: Workload Identity binding detected (IRSA)

# 3. See the attack chain
# Pod (default/app) 
#   → Can create pods (RBAC)
#   → Create privileged pod with hostPath:/ (node escape)
#   → Access /var/lib/kubelet/kubeconfig (steal higher-priv token)
#   → Use stolen token to query all secrets (including IRSA token)
#   → IRSA token → AWS STS credentials
#   → AWS CLI: aws s3 ls → s3://prod-database-backups/
#   → Exfil production database

# 4. Generated PoC (from KubeXHunt output):
# kubectl create pod privileged-escape --image=alpine ... hostPath:/ ...
# export TOKEN=$(curl http://169.254.169.254/latest/api/token -H "X-aws-ec2-metadata-token-ttl-seconds: 21600")
# curl http://169.254.169.254/latest/meta-data/iam/security-credentials -H "X-aws-ec2-metadata-token: $TOKEN"

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Phases Overview

Phase	Name	What It Does	Opsec
0	Setup	Bootstrap (install kubectl, detect runtime)	🟢 QUIET
1	Pod Recon	Enumerate SA, mounts, capabilities, env	⚪ SILENT
2	Cloud Metadata	Check IMDS, GCP, Azure endpoints	🟢 QUIET
3	RBAC	Enumerate roles, bindings, escalation paths	🟡 MEDIUM
4	Network	Port scanning, DNS brute, reachability	🔴 LOUD
5	Escape	Kernel CVE detection, seccomp bypass	🔴 LOUD
6	Admission	Check webhook policies, bypass techniques	🟡 MEDIUM
7	Kubelet	Enumerate 10250, 10255, pod credential harvesting	🟡 MEDIUM
8	Privilege Escalation	Pod/role creation, RBAC binding chains	🔴 LOUD
9	Supply Chain	Image pull secrets, registry probing	🟡 MEDIUM
10–12	Cloud Platforms	EKS, GKE, Azure detection & enumeration	🟢 QUIET
13	Secrets	Enumerate all Kubernetes secrets	🔴 LOUD
14	DoS	Resource exhaustion, CrashLoopBackOff attacks	🔴 LOUD
15+	Advanced	Cluster intelligence, stealth analysis, etc.	🟡 MEDIUM

Use --stealth to skip LOUD phases in production:

python3 kubexhunt.py --stealth 2 --no-mutate  # Skip LOUD & MEDIUM, read-only

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Key Features

1. Structured Findings

Every finding includes:

• MITRE ATT&CK for Containers technique IDs (e.g., T1078.001 Valid Accounts)
• CWE and CVSS 3.1 scores
• CIS Kubernetes Benchmark control mappings
• Remediation steps with effort estimates
• Attack paths showing how this finding chains to compromise

Example finding (JSON):

{
  "id": "RBAC-WILDCARD-001",
  "title": "ClusterRole with overly permissive wildcard verbs",
  "severity": "CRITICAL",
  "confidence": 0.95,
  "mitre": ["T1078.001", "T1087.002"],
  "cwe": ["CWE-276"],
  "attack_paths": [
    {
      "path_id": "PATH-001",
      "nodes": ["sa:default:app", "clusterrole:viewer", "secret:kube-system:admin-token", "CLUSTER_ADMIN"],
      "steps": ["Use SA to list ClusterRoles", "Find wildcard verbs", "Read all secrets", "Extract admin token"]
    }
  ],
  "remediation": {
    "summary": "Restrict ClusterRole permissions to only required verbs",
    "steps": [
      {"step": "kubectl patch clusterrole viewer ...", "effort": "LOW"}
    ]
  }
}

2. Attack Path Graphs

Visualize your route to cluster compromise:

[Compromised Pod]
  ↓ CAN_CREATE_POD (RBAC)
[Malicious Pod with hostPath:/]
  ↓ CAN_ESCAPE_TO_NODE
[Node Root Shell]
  ↓ MOUNTS_ALL_SA_TOKENS
[Stolen Cluster-Admin Token]
  ↓ CAN_IMPERSONATE
[Cluster Admin]

Export as:

• D3.js interactive HTML (click to zoom/filter)
• Graphviz DOT (render with dot -Tpng)
• Neo4j Cypher queries (for further analysis)

3. Opsec Ratings

Each check is rated for detectability:

• ⚪ SILENT — Indistinguishable from normal behavior
• 🟢 QUIET — Hard to detect without dedicated monitoring
• 🟡 MEDIUM — Visible in standard audit logs
• 🔴 LOUD — Obvious anomalies, immediate detection

Use stealth levels to balance coverage vs. noise:

--stealth 0   # Run all phases (maximum noise)
--stealth 1   # Skip LOUD phases (good balance)
--stealth 2   # Skip LOUD + MEDIUM (minimal noise, limited coverage)

4. Multi-Output Formats

python3 kubexhunt.py --output report.json      # Machine-readable
python3 kubexhunt.py --output report.html      # Interactive UI
python3 kubexhunt.py --output report.sarif     # CI/CD integration
python3 kubexhunt.py --output report.txt       # Console-friendly
python3 kubexhunt.py --output report.graphviz  # Attack graphs

5. Diff and Trending

# Compare against a previous scan
python3 kubexhunt.py --diff previous.json

# Shows: new findings, resolved findings, regressions

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Installation

Via PyPI (coming soon)

pip install kubexhunt

Via Homebrew (coming soon)

brew install kubexhunt

Via Krew (kubectl plugin)

kubectl krew install kubexhunt
kubexhunt scan --output report.json

From Source

git clone https://github.com/mr-xhunt/kubeX.git
cd kubexhunt
pip install -e .
python3 -m kubexhunt --help

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Use Cases

Red Teams

• Simulate post-breach K8s cluster compromise
• Generate realistic attack scenarios
• Test incident response workflows

Security Engineering

• Continuous cluster monitoring (run nightly)
• Regression detection (compare scans over time)
• CISO reporting (HTML/SARIF for dashboards)

DevOps / Platform Teams

• Pre-prod cluster hardening validation
• Compliance audits (CIS Benchmark mapping)
• Incident investigation ("was this cluster already compromised?")

Security Researchers

• Graph-based cluster analysis
• Novel privilege escalation paths
• CVE-to-K8s-exploitation chains

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Comparison to Alternatives

Feature	KubeXHunt	KubeHound	kube-hunter	Peirates
Attack-path chaining	✅ Automated	✅ Graph DB	❌ No	❌ Manual
In-cluster execution	✅ Agent	❌ Agentless	✅ Agent	✅ Agent
Cloud pivoting	✅ K8s→AWS/GCP/Azure	❌ K8s only	❌ No	❌ No
Zero external dependencies	✅ Yes	❌ Neo4j	✅ Yes	✅ Yes
Exploitation automation	✅ Shell scripts	❌ No	❌ No	❌ No
MITRE ATT&CK mapping	✅ Per-finding	❌ No	❌ No	❌ No
Opsec ratings	✅ SILENT/QUIET/MEDIUM/LOUD	❌ No	❌ No	❌ No
Maintained	✅ Active	✅ Active	❌ Archived	❌ Archived
GitHub stars	TBD	2.8k	4.2k	2.0k

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Documentation

• Getting Started Guide — Installation and first scan
• Phase Reference — Detailed description of each phase
• Architecture — System design and module overview
• Contributing — How to add new features and fixes
• Security Policy — Responsible vulnerability disclosure
• Roadmap — Future directions and priorities

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Examples

Scan Your Test Cluster (read-only)

export KUBECONFIG=/path/to/kubeconfig
python3 kubexhunt.py --no-mutate --output test-cluster-report.json

Simulate Post-Breach Attack Chain

python3 kubexhunt.py --mutate --exploit daemonset-root --output attacker-poc.json

CI/CD Integration (SARIF for GitHub Security)

python3 kubexhunt.py --output sarif-report.sarif --phase 1 2 3
# Upload to GitHub via:
# gh api repos/OWNER/REPO/code-scanning/sarifs --input sarif-report.sarif

Compare Scan Results

python3 kubexhunt.py --output today.json --diff yesterday.json
# Shows: NEW findings, FIXED findings, REGRESSIONS

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Community & Support

• GitHub Issues — Report bugs
• Discussions — Ask questions
• Contributing — Add features
• Security — Report vulnerabilities

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License

MIT License. See LICENSE for details.

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Acknowledgments

KubeXHunt was inspired by:

• Peirates (InGuardians) — post-compromise K8s exploitation
• kube-hunter (Aqua) — K8s vulnerability discovery
• BloodHound (SpecterOps) — AD attack graph model
• KubeHound (Datadog) — Kubernetes attack graph

Special thanks to the Kubernetes security community for research and feedback.

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Disclaimer

KubeXHunt is a security assessment tool designed for authorized testing only.

• Only run against clusters you own or have explicit written authorization to test
• Unauthorized access to computer systems is illegal
• Authors assume no liability for misuse

See SECURITY.md for responsible disclosure guidelines.

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Ready to assess your cluster?

python3 kubexhunt.py --output report.html
open report.html