Claude 24f816b6a3
Consolidate 22 sibling repos into layered organism structure
Place useful parts of the surrounding repos into sica-fondt by layer, per the
body model (Ada = membrane; brain/endocrine/capabilities/knowledge non-Ada):

- brain/        LLM reasoning + providers (dapr, hermes, MoMoA)
- capabilities/ REPRAG sidecars: hermes tools/skills, dapr tools, parallel
                dispatch, A51 channels, and the OSINT cluster
- knowledge/    LORAG corpus: 754 cyber-skills, agency personas, secure-coding,
                MITRE ATT&CK data
- reference/    defensive threat-reference (C3, shhbruh doc) + AdaYaml parser

License handling: AGPL sources (worldosint, advanced_evolution, mercury,
Reticulum) and GPL DeTTECT are SPEC-only clean-room/port descriptions — no
copyleft code copied. MIT/Apache/data parts copied as working trees.

Safety: shhbruh escape/persistence material and C3 covert-C2 kept as reference
only, not wired into the running organism. See CONSOLIDATION.md.

https://claude.ai/code/session_01UehUqEXXJJCsHoA4voCU5c
2026-06-10 06:53:01 +00:00

2.1 KiB

name description domain subdomain tags version author license nist_csf
analyzing-kubernetes-audit-logs Parses Kubernetes API server audit logs (JSON lines) to detect exec-into-pod, secret access, RBAC modifications, privileged pod creation, and anonymous API access. Builds threat detection rules from audit event patterns. Use when investigating Kubernetes cluster compromise or building k8s-specific SIEM detection rules. cybersecurity container-security
analyzing
kubernetes
audit
logs
1.0 mahipal Apache-2.0
PR.PS-01
PR.IR-01
ID.AM-08
DE.CM-01

Analyzing Kubernetes Audit Logs

When to Use

  • When investigating security incidents that require analyzing kubernetes audit logs
  • When building detection rules or threat hunting queries for this domain
  • When SOC analysts need structured procedures for this analysis type
  • When validating security monitoring coverage for related attack techniques

Prerequisites

  • Familiarity with container security concepts and tools
  • Access to a test or lab environment for safe execution
  • Python 3.8+ with required dependencies installed
  • Appropriate authorization for any testing activities

Instructions

Parse Kubernetes audit log files (JSON lines format) to detect security-relevant events including unauthorized access, privilege escalation, and data exfiltration.

import json

with open("/var/log/kubernetes/audit.log") as f:
    for line in f:
        event = json.loads(line)
        verb = event.get("verb")
        resource = event.get("objectRef", {}).get("resource")
        user = event.get("user", {}).get("username")
        if verb == "create" and resource == "pods/exec":
            print(f"Pod exec by {user}")

Key events to detect:

  1. pods/exec and pods/attach (shell into containers)
  2. secrets access (get/list/watch)
  3. clusterrolebindings creation (RBAC escalation)
  4. Privileged pod creation
  5. Anonymous or system:unauthenticated access

Examples

# Detect secret enumeration
if verb in ("get", "list") and resource == "secrets":
    print(f"Secret access: {user} -> {event['objectRef'].get('name')}")