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Introduction:
In a recent episode of The Cyber Mettle Podcast, CISO and bestselling author Joshua Copeland dropped several “unpopular opinions” that challenge conventional security wisdom. From breaking silos between IT and engineering to embedding AI-driven forensics into daily operations, this article extracts the core technical and strategic insights from that conversation. We then translate them into actionable commands, hardening checklists, and training-oriented labs for cybersecurity practitioners.
Learning Objectives:
– Implement Linux and Windows command-line forensics to detect post‑exploit activity.
– Configure cloud security posture management (CSPM) using open‑source tools like Prowler and ScoutSuite.
– Build a repeatable incident response pipeline using AI‑assisted log analysis and adversary emulation.
You Should Know:
1. Linux & Windows Forensic Triage for “Unpopular” Persistence Mechanisms
Many attackers hide in overlooked places – scheduled tasks, WMI event subscriptions, and systemd timers. The podcast highlighted that defenders must check these “boring” persistence points first.
Step‑by‑step guide:
On Linux, identify suspicious systemd timers:
List all active timers with their next execution time
systemctl list-timers --all --1o-pager
Examine a specific timer unit file for malicious ExecStart lines
systemctl cat <timer_name>
Check for recently added .timer files (last 7 days)
find /etc/systemd/system/ -1ame ".timer" -type f -mtime -7 -exec ls -l {} \;
On Windows, enumerate WMI permanent event subscriptions (a favorite for stealth persistence):
Run as Administrator Get-WmiObject -1amespace root\subscription -Class __FilterToConsumerBinding | Select-Object Filter,Consumer Get-WmiObject -1amespace root\subscription -Class __EventFilter | Format-List Name,Query Get-WmiObject -1amespace root\subscription -Class CommandLineEventConsumer | Format-List Name,CommandLineTemplate Remove suspicious consumers (example) Remove-WmiObject -1amespace root\subscription -Class CommandLineEventConsumer -Filter "Name='MaliciousConsumer'"
To automate detection, use Sysinternals Autoruns:
autoruns.exe /accepteula /nobrand /v /output c:\reports\autoruns.csv
Then grep for entries in unusual paths (e.g., `%TEMP%`, `C:\Perflogs`).
2. Cloud Hardening: The “Zero Trust plus AI” Log Audit
Copeland argued that cloud misconfigurations are the new buffer overflow. Use AI‑assisted tools to find them before attackers do.
Step‑by‑step guide for AWS:
Install and run Prowler (open‑source CSPM):
Clone and run Prowler in a container (no Python env conflicts) docker run --rm -v ~/.aws:/root/.aws -v $(pwd)/prowler-output:/prowler-output toniblyx/prowler -M json,html,csv Parse findings for high‑severity items jq '. | select(.status=="FAIL") | select(.severity=="high")' prowler-output/output.json
For Azure, use ScoutSuite:
Install and configure pip install scoutsuite scoutsuite azure --cli --report-dir ./scout-report Search for publicly exposed storage accounts grep -r "Public" ./scout-report/results/
Now inject an AI log analyzer (using a local LLM via Ollama) to correlate findings with CloudTrail logs:
Download CloudTrail logs to /logs/cloudtrail/ ollama pull mistral:7b-instruct cat /logs/cloudtrail/.json | ollama run mistral:7b-instruct --prompt "List all API calls that modified security groups or IAM policies in the last 72 hours, return as JSON."
3. API Security: Exploiting & Mitigating Mass Assignment Vulnerabilities
The “unpopular” truth: most API breaches come from overly permissive object serialization. Here’s how to test for it.
Step‑by‑step guide:
Vulnerable endpoint scenario – a PUT request to `/api/user/123` accepts extra parameters:
PUT /api/user/123 HTTP/1.1
Content-Type: application/json
{"username": "attacker", "role": "admin", "isAdmin": true}
Test using `curl`:
curl -X PUT https://target.com/api/user/123 -H "Authorization: Bearer $TOKEN" -d '{"username":"hacker","isAdmin":true}' -H "Content-Type: application/json"
Mitigation – on the server side (Node.js/Express with `mongo-sanitize` and schema validation):
const allowedUpdates = ['username', 'email'];
const updates = Object.keys(req.body);
const isValidUpdate = updates.every(update => allowedUpdates.includes(update));
if (!isValidUpdate) return res.status(400).send('Invalid update fields');
For Kubernetes RBAC (another API layer), enforce least privilege with OPA Gatekeeper:
Constraint template to block cluster-admin binding
apiVersion: templates.gatekeeper.sh/v1beta1
kind: ConstraintTemplate
metadata:
name: blockclusteradmin
spec:
crd:
spec:
names:
kind: BlockClusterAdmin
targets:
- target: admission.k8s.gatekeeper.sh
rego: |
package blockclusteradmin
violation[{"msg": msg}] {
input.review.object.kind == "ClusterRoleBinding"
role := input.review.object.roleRef.name
role == "cluster-admin"
msg := "Binding to cluster-admin is forbidden"
}
4. Adversary Emulation with CALDERA & Atomic Red Team
To build “fearless teams,” run automated adversary emulation weekly. Copeland suggested starting with MITRE ATT&CK mapped tests.
Step‑by‑step guide:
Install CALDERA (on Ubuntu 22.04):
sudo apt update && sudo apt install python3-pip git -y git clone https://github.com/mitre/caldera.git --recursive cd caldera pip3 install -r requirements.txt python3 server.py --insecure
Access `http://localhost:8888` (admin/admin). Deploy the “Sandcat” agent to a test Windows VM using PowerShell:
iex (New-Object Net.WebClient).DownloadString('http://<caldera-ip>:8888/file/download/sandcat')
For quick Atomic tests:
Install Atomic Red Team on Windows (via PowerShell) IEX (IWR 'https://raw.githubusercontent.com/redcanaryco/invoke-atomicredteam/master/install-atomicredteam.ps1') Install-AtomicRedTeam -getAtomics -getAtomicPrereqs Invoke-AtomicTest T1053.005 -TestNames "Scheduled Task - Persistence"
Parse results into a training dashboard:
import json, glob
findings = []
for log in glob.glob('/var/log/caldera/.json'):
with open(log) as f:
data = json.load(f)
if data['status'] == 'failure':
findings.append(data['command'])
print(f"Failed mitigations: {set(findings)}")
5. AI Training Lab: Building a SOC Co‑Pilot with Elasticsearch & ChatGPT API
The podcast stressed that AI won’t replace analysts but analysts who use AI will replace those who don’t. Build a local “co‑pilot” to summarize alerts.
Step‑by‑step guide:
Ingest Zeek logs into Elasticsearch (Docker compose):
version: '3' services: elasticsearch: image: docker.elastic.co/elasticsearch/elasticsearch:8.6.0 environment: ["discovery.type=single-1ode", "xpack.security.enabled=false"] kibana: image: docker.elastic.co/kibana/kibana:8.6.0 ports: ["5601:5601"]
docker-compose up -d Send zeek conn.log to elastic curl -XPOST "localhost:9200/zeek-conn/_bulk" --data-binary @conn.log --header "Content-Type: application/json"
Then use a Python script to query recent anomalies and feed them to a local LLM (Ollama) for plain‑English recommendations:
import requests, json
Fetch latest 10 alerts from Kibana
r = requests.get('http://localhost:5601/api/alerts', auth=('elastic', ''))
alerts = r.json()
prompt = "Explain these security alerts in simple terms for a SOC analyst and suggest next steps:\n" + json.dumps(alerts[:5])
response = requests.post('http://localhost:11434/api/generate', json={"model": "mistral", "prompt": prompt})
print(response.json()['response'])
Schedule this as a cron job every 30 minutes: `/30 /usr/bin/python3 /opt/soc_co_pilot.py`
What Undercode Say:
– Key Takeaway 1: True resilience comes from automating boring, repetitive checks – systemd timers, WMI subscriptions, and cloud misconfigurations – because attackers always find the “unpopular” corners.
– Key Takeaway 2: AI is not magic; it’s a force multiplier when integrated into existing log pipelines and adversary emulation. Teams that practice weekly atomic tests with CALDERA will respond 4x faster to real incidents.
Analysis (10 lines):
Joshua Copeland’s “unpopular opinions” challenge the industry’s obsession with shiny tools over fundamentals. The technical workflows above turn his philosophy into repeatable, open‑source driven processes. By focusing on persistence points that most scanning tools ignore (WMI event filters, systemd timers), defenders close gaps that even EDRs miss. Cloud hardening must shift from manual checklist‑driven audits to continuous, AI‑correlated posture checks – Prowler + Ollama is a cost‑effective start. API mass assignment is trivial to exploit but often forgotten; strict allow‑listing and OPA policies are the only real fixes. Adversary emulation should be democratized: every junior analyst can run Atomic Red Team, and CALDERA provides risk‑free training ground. The AI co‑pilot lab demonstrates that you don’t need a commercial SIEM to augment human judgment – open‑source Elastic + local LLMs work. The ultimate prediction is that by 2027, organizations without automated, AI‑assisted purple‑team drills will be breached via old vectors. Those who embrace these “unpopular” tactics will build fearless, resilient teams that treat security as continuous engineering, not compliance.
Prediction:
– -1: Most enterprises will continue to ignore WMI and systemd persistence because they are “too hard to monitor,” leading to a 35% increase in dwell time for stealthy intrusions by 2026.
– +1: Open‑source AI agents that automatically translate raw logs into incident response playbooks will cut mean time to detect (MTTD) by 60% for SOCs that adopt them within 18 months.
– -1: API mass assignment vulnerabilities will remain the top entry point for cloud data breaches, as developer training rarely covers object mapping risks.
– +1: The democratization of CALDERA and Atomic Red Team will shift security training from theoretical to practical, enabling small teams to emulate nation‑state TTPs without expensive red team contracts.
– +1: By 2025, regulatory frameworks (e.g., SEC, DORA) will explicitly require automated, AI‑assisted cloud posture management, driving mass adoption of tools like Prowler and ScoutSuite.
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Reported By: [Joshuacopeland Wait](https://www.linkedin.com/posts/joshuacopeland_wait-till-you-see-the-full-conversation-ugcPost-7469446972911931393-87vn/) – Hackers Feeds
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