external/anthropic-cybersecurity-skills/skills/implementing-security-chaos-engineering/SKILL.md
Implements security chaos engineering experiments that deliberately disable or degrade security controls to verify detection and response capabilities. Tests WAF bypass, firewall rule removal, log pipeline disruption, and EDR disablement scenarios using boto3 and subprocess. Use when validating SOC detection coverage and resilience.
npx skillsauth add seikaikyo/dash-skills implementing-security-chaos-engineeringInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Design and execute security chaos experiments that intentionally break security controls to verify that detection, alerting, and response systems work correctly.
# Example: Verify detection when a security group is opened
import boto3
ec2 = boto3.client("ec2")
# Chaos experiment: temporarily add 0.0.0.0/0 rule
ec2.authorize_security_group_ingress(
GroupId="sg-12345",
IpProtocol="tcp", FromPort=22, ToPort=22,
CidrIp="0.0.0.0/0",
)
# Verify: does GuardDuty/Config alert fire within SLA?
# Rollback: remove the rule after verification
Key experiments:
# Rollback function for safe experiment execution
def run_experiment(setup_fn, verify_fn, rollback_fn, timeout=300):
try:
setup_fn()
result = verify_fn(timeout)
finally:
rollback_fn()
return result
tools
Conduct comprehensive GDPR compliance assessments by evaluating data processing activities against EU Regulation 2016/679, including Article 30 records of processing, lawful basis validation, data subject rights implementation, Data Protection Impact Assessments (DPIAs) under Article 35, breach notification procedures, international transfer safeguards (SCCs, adequacy decisions), and technical/organizational measures under Article 32. Use when processing personal data of EU residents, preparing for supervisory authority audits, implementing privacy-by-design for new systems, scoping compliance gaps for M&A due diligence, assessing third-party processors, or responding to data subject access requests at scale. Incorporates 2026 guidance from ICO, EDPB, and post-Data (Use and Access) Act 2025 UK-GDPR considerations. Do not use for implementing specific Article 32 controls — use implementing-gdpr-data-protection-controls; or for DSAR automation — use implementing-gdpr-data-subject-access-request.
tools
Parse Windows forensic artifacts—$MFT/$J (MFTECmd), Prefetch (PECmd), registry hives (RECmd), shellbags, and Amcache—into normalized CSV/JSON with Eric Zimmerman's EZ Tools, then load results into Timeline Explorer for analysis. Use during DFIR/incident-response investigations, after triage collection (e.g. with KAPE), to establish program execution, file/folder access, and persistence evidence from acquired forensic images.
development
Build automated multi-turn adversarial attacks against conversational LLM targets using Microsoft PyRIT's RedTeamingOrchestrator, CrescendoOrchestrator (gradual escalation), and TreeOfAttacksWithPruningOrchestrator (adaptive branching), with scorer feedback loops and persisted conversation memory. Use when single-shot LLM scanning is insufficient and you need multi-turn, scorer-driven AI red-team campaigns against a chatbot or agent.
testing
Stand up MISP, enable and cache curated threat feeds (CIRCL, abuse.ch, Feodo Tracker), apply warninglists to suppress false positives, query indicators with PyMISP, and export attributes as auto-generated Suricata/Sigma/Wazuh detection rules. Use when maturing a MISP instance to actively drive detection, curating threat feeds with quality controls, or automating IOC-to-detection pipelines for the SIEM/IDS.