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Introduction:
As vulnerability research accelerates with AI-assisted workflows, security teams face a paradox: more findings than ever, yet less time to validate them. The fusion of large language models with automated reconnaissance tools has slashed researcher time-to-exploit from days to hours, while bug bounty platforms like YesWeHack report unprecedented submission volumes. At the upcoming AI Security Forum hosted by the Global Digital Foundation, YesWeHack and Huawei will unveil how Turkish enterprises are operationalizing AI for vulnerability detection and remediation—shifting the bottleneck from discovery to intelligent triage.
Learning Objectives:
– Implement AI-augmented vulnerability scanning workflows using open-source tools and custom LLM prompts
– Automate triage of bug bounty reports with severity scoring and duplicate detection using Python and machine learning
– Harden cloud and API assets against AI-generated attack patterns using proactive configuration controls
You Should Know:
1. Automating AI-Assisted Vulnerability Scanning with Nuclei + LLM Integration
Modern bug bounty hunters combine Nuclei’s template engine with local LLMs (e.g., Ollama running CodeLlama) to auto-generate custom detection logic for new endpoints. This approach reduces manual template writing by 70% while increasing coverage for business logic flaws.
Step‑by‑step guide (Linux):
Install Nuclei and Ollama
curl -s https://raw.githubusercontent.com/projectdiscovery/nuclei/master/install.sh | bash
curl -fsSL https://ollama.com/install.sh | sh
ollama pull codellama:7b
Create a wrapper script to generate Nuclei templates from HTTP responses
cat > ai_nuclei_gen.py << 'EOF'
import subprocess, json, sys
def gen_template(response_text):
prompt = f"Generate a Nuclei YAML template for vulnerability: {response_text[:500]}\nOutput only YAML."
result = subprocess.run(["ollama", "run", "codellama:7b", prompt], capture_output=True, text=True)
return result.stdout
if __name__ == "__main__":
print(gen_template(sys.argv[bash]))
EOF
Run a basic scan and pipe findings to the AI generator
nuclei -u https://example.com -json | jq -r '.host' | while read url; do
curl -s "$url" | python3 ai_nuclei_gen.py > custom_${url//\//_}.yaml
done
Windows PowerShell equivalent:
Install nuclei via WSL or scoop, then use Invoke-WebRequest
$response = Invoke-WebRequest -Uri "https://example.com" -UseBasicParsing
$prompt = "Generate a Nuclei template for: $($response.Content.Substring(0,500))"
Call Ollama via REST API (if running locally)
$body = @{model="codellama:7b"; prompt=$prompt} | ConvertTo-Json
Invoke-RestMethod -Uri "http://localhost:11434/api/generate" -Method Post -Body $body -ContentType "application/json"
How to use: Run the script against staging endpoints before bug bounty launches. The AI-generated templates catch edge cases like reflected parameters in JSON bodies—often missed by static rules.
2. Intelligent Triage: Reducing False Positives with ML Classification
Rising report volumes (YesWeHack notes 200% year-over-year increases for some programs) demand automated severity scoring. Using scikit-learn, you can train a classifier on historical bug bounty reports to predict CVSS scores.
Step‑by‑step guide (Python environment):
Install dependencies pip install pandas scikit-learn transformers torch
triage_model.py
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.ensemble import RandomForestClassifier
from sklearn.pipeline import Pipeline
Example training data (replace with your CSV: title, description, actual_severity)
df = pd.read_csv("bug_reports.csv")
X = df["title"] + " " + df["description"]
y = df["actual_severity"] 0=info, 1=low, 2=medium, 3=high, 4=critical
pipeline = Pipeline([
("tfidf", TfidfVectorizer(max_features=5000, ngram_range=(1,2))),
("rf", RandomForestClassifier(n_estimators=100))
])
pipeline.fit(X, y)
Predict new submission
new_report = "SQL injection in login parameter 'user' with time-based blind"
print("Predicted severity:", pipeline.predict([bash])[bash])
Deployment on Windows: Use Task Scheduler to run this script every hour on a shared inbox. When a report scores critical (4), auto-escalate via webhook to Slack or Jira.
3. Hardening APIs Against AI-Generated Payloads
Attackers now use LLMs to mutate known payloads, bypassing signature-based WAFs. For example, a simple `’ OR 1=1 –` becomes `’ OR 1=1 AND SLEEP(5) /` plus random case variations. Defend by implementing request normalization and anomaly detection.
Linux commands for API hardening (nginx + ModSecurity):
Install ModSecurity with CRS3 sudo apt install libmodsecurity3 nginx-modsecurity sudo cp /etc/modsecurity/modsecurity.conf-recommended /etc/modsecurity/modsecurity.conf sudo sed -i 's/SecRuleEngine DetectionOnly/SecRuleEngine On/' /etc/modsecurity/modsecurity.conf Add rule to block SQLi variants with regex length limits echo 'SecRule ARGS "@rx (?i)(select|union|sleep|benchmark|waitfor)" "id:1001,phase:2,deny,status:403,msg:'\'AI SQLi Variant\'""' | sudo tee -a /etc/modsecurity/owasp-crs/rules/REQUEST-942-APPLICATION-ATTACK-SQLI.conf
Windows (IIS with URL Rewrite):
Create an outbound rule that inspects query strings for common mutation patterns like `%20%27%20OR` or `WAITFOR%20DELAY`. Use PowerShell to automate rule deployment via `New-WebConfigurationProperty`.
4. Cloud Hardening for AI-Driven Reconnaissance
AI agents crawl serverless functions and storage buckets at scale, searching for misconfigured IAM roles. Use infrastructure-as-code scanning to detect overly permissive policies before deployment.
AWS CLI commands (Linux/macOS):
Scan all S3 buckets for public ACLs
aws s3api list-buckets --query 'Buckets[].Name' --output text | tr '\t' '\n' | while read bucket; do
acl=$(aws s3api get-bucket-acl --bucket $bucket)
if echo $acl | grep -q '"URI":"http://acs.amazonaws.com/groups/global/AllUsers"'; then
echo "WARNING: $bucket is public"
fi
done
Enforce bucket policies with Condition block to block AI scrapers
aws s3api put-bucket-policy --bucket secure-bucket --policy '{
"Version":"2012-10-17",
"Statement":[{
"Effect":"Deny",
"Principal":"",
"Action":"s3:GetObject",
"Resource":"arn:aws:s3:::secure-bucket/",
"Condition":{"NotIpAddress":{"aws:SourceIp":"192.0.2.0/24"}}
}]
}'
Azure alternative (PowerShell):
Get-AzStorageAccount | Get-AzStorageContainer | Get-AzStorageContainerAcl | Where-Object { $_.PublicAccess -1e "Off" }
5. Exploiting and Patching AI‑Generated XSS Vectors
Attackers use LLMs to craft polyglot XSS payloads that evade CSP. Example: `”>` becomes `”>
