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Introduction:
AI agents are no longer just assisting security researchers—they are actively finding real bugs, topping bug bounty leaderboards, and generating so much traffic that programs like cURL have shut down their entire bounty initiative due to spam overload. The hackers who adapt by building custom, scoped AI agents are discovering more vulnerabilities in a week than traditional methods yielded in a month, leaving manual hunters who refuse to evolve facing obsolescence.
Learning Objectives:
- Understand how AI agents automate reconnaissance, fuzzing, and vulnerability discovery in bug bounty programs
- Build and deploy a custom AI-powered agent using local LLMs or cloud APIs for subdomain enumeration, payload generation, and report triage
- Implement filtering mechanisms to distinguish valid findings from AI-generated noise, ensuring your reports get paid instead of ignored
You Should Know:
- Setting Up Your AI Bug Hunting Environment (Linux)
AI agents require a foundation of tools and models. Start by installing a local LLM to avoid API costs and rate limits, then integrate it with traditional security tooling.
Step‑by‑step guide:
Update system and install dependencies sudo apt update && sudo apt install -y python3 python3-pip git curl jq Install Ollama for local LLM inference curl -fsSL https://ollama.com/install.sh | sh ollama pull llama3.2:3b Lightweight model for bug hunting ollama pull deepseek-coder:6.7b For code analysis Create a Python virtual environment python3 -m venv ai-hunter source ai-hunter/bin/activate Install core hacking tools go install -v github.com/projectdiscovery/subfinder/v2/cmd/subfinder@latest go install -v github.com/ffuf/ffuf@latest go install -v github.com/tomnomnom/waybackurls@latest
What this does: Ollama runs LLMs locally, eliminating network latency and keeping your reconnaissance private. Subfinder, ffuf, and waybackurls are standard for asset discovery. The environment is now ready for AI orchestration.
- Building a Custom AI Agent for Subdomain Enumeration
Instead of running subfinder alone, an AI agent can dynamically adjust its scope, filter false positives, and prioritize targets based on live feedback.
Step‑by‑step guide (Python script):
!/usr/bin/env python3
import subprocess, json, requests
def query_llm(prompt):
response = requests.post('http://localhost:11434/api/generate',
json={'model': 'llama3.2:3b', 'prompt': prompt, 'stream': False})
return response.json()['response']
def run_subfinder(domain):
result = subprocess.run(['subfinder', '-d', domain, '-silent'], capture_output=True, text=True)
subdomains = result.stdout.splitlines()
AI agent filters noise
filtered = query_llm(f"Return only valid subdomains from this list, remove any that look like CDNs or parked domains: {subdomains[:20]}")
return filtered
if <strong>name</strong> == '<strong>main</strong>':
target = input("Enter target domain: ")
live_subs = run_subfinder(target)
print(f"AI-filtered subdomains:\n{live_subs}")
Usage: Save as ai_subfinder.py, run python3 ai_subfinder.py example.com. The LLM removes irrelevant entries (e.g., www.example.com.cdn.cloudflare.net), saving hours of manual triage.
- Automating Payload Generation with AI for XSS and SQLi
AI can craft context‑aware payloads that bypass traditional WAF rules, adapting to input reflections in real time.
Step‑by‑step guide using curl and Ollama:
Extract URLs from a domain
waybackurls example.com | sort -u > urls.txt
AI generates custom XSS payloads based on URL parameters
cat urls.txt | while read url; do
param=$(echo $url | grep -oP '(?<=\?).' | cut -d'=' -f1 | head -1)
if [[ -n $param ]]; then
prompt="Generate 3 XSS payloads for a parameter named '$param' that bypass common filters like htmlspecialchars. Output only the payloads."
curl -s http://localhost:11434/api/generate -d "{\"model\":\"llama3.2:3b\",\"prompt\":\"$prompt\",\"stream\":false}" | jq -r '.response'
fi
done > xss_payloads.txt
Fuzz with ffuf using AI-generated payloads
ffuf -u "https://example.com/page?param=FUZZ" -w xss_payloads.txt -ac
Explanation: The AI examines each parameter name and generates payloads tailored to evade common sanitization (e.g., `
- Filtering False Positives and Spam Reports (Windows & Linux)
cURL shut down because AI spam overwhelmed their triage. You must pre‑filter your findings before submitting.
Step‑by‑step guide (cross‑platform with PowerShell and bash):
Windows (PowerShell):
Assume you have a list of potential findings in findings.json
$findings = Get-Content -Path .\findings.json | ConvertFrom-Json
$apiKey = "your-openai-key"
foreach ($f in $findings) {
$body = @{
model = "gpt-3.5-turbo"
messages = @(
@{role = "system"; content = "You are a bug bounty triager. Respond with 'VALID' or 'SPAM' only."}
@{role = "user"; content = "Vulnerability claim: $($f.description) on $($f.url)"}
)
} | ConvertTo-Json
$response = Invoke-RestMethod -Uri "https://api.openai.com/v1/chat/completions" -Method Post -Headers @{"Authorization"="Bearer $apiKey"} -Body $body -ContentType "application/json"
if ($response.choices[bash].message.content -eq "VALID") {
$f | Export-Csv -Append -Path valid_findings.csv
}
}
Linux (using local LLM for offline filtering):
Create a triage script
cat > triage.sh << 'EOF'
!/bin/bash
while IFS= read -r report; do
prompt="Classify this bug report as VALID or SPAM: $report"
result=$(curl -s http://localhost:11434/api/generate -d "{\"model\":\"llama3.2:3b\",\"prompt\":\"$prompt\",\"stream\":false}" | jq -r '.response')
if [[ "$result" == "VALID" ]]; then echo "$report" >> valid.txt; fi
done < raw_reports.txt
EOF
chmod +x triage.sh
./triage.sh
Why this matters: Programs pay for valid findings. Running every raw AI output through a second LLM classifier reduces noise by 70‑90%, preserving your reputation and payout rate.
- Hardening Your AI Agent Against API Rate Limits and IP Bans
Aggressive scanning triggers WAF blocks. Use proxy rotation and request throttling integrated with AI decision‑making.
Step‑by‑step guide with AI‑controlled delays:
import time, random, requests
from stem import Signal
from stem.control import Controller
Tor proxy rotation
with Controller.from_port(port=9051) as controller:
controller.authenticate()
controller.signal(Signal.NEWNYM)
proxies = {'http': 'socks5h://127.0.0.1:9050', 'https': 'socks5h://127.0.0.1:9050'}
def smart_request(url):
AI decides delay based on target responsiveness
prompt = f"Given a target at {url}, recommend a delay in seconds between requests (0.5 to 10) to avoid rate limiting but stay fast."
delay = float(requests.post('http://localhost:11434/api/generate',
json={'model':'llama3.2:3b','prompt':prompt,'stream':False}).json()['response'])
time.sleep(delay)
return requests.get(url, proxies=proxies, timeout=10)
Implementation: Run `tor` in background, then execute the script. The AI agent dynamically adjusts sleep intervals—longer delays when errors (429/503) appear, shorter when everything succeeds.
- Windows‑Based AI Agent Using WSL and OpenAI API
For Windows users, combine WSL’s Linux toolchain with cloud AI APIs for maximum performance.
Step‑by‑step guide:
Enable WSL and install Ubuntu
wsl --install -d Ubuntu
Inside WSL, install tools as in Section 1, but use OpenAI API instead of local LLM
export OPENAI_API_KEY="your-key"
Create a PowerShell wrapper to call WSL commands
$script = @"
subfinder -d $target -silent | while read sub; do
prompt="Generate a nuclei template for testing subdomain takeover on $sub"
curl -s https://api.openai.com/v1/chat/completions \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-H "Content-Type: application/json" \
-d "{\"model\":\"gpt-4\",\"messages\":[{\"role\":\"user\",\"content\":\"$prompt\"}]}"
done
"@
wsl bash -c "$script"
Usage: Replace `$target` with your scope. The AI generates custom Nuclei templates on the fly, then you can run `nuclei -t generated.yaml` to test.
- Mitigating AI‑Generated Spam from Other Hunters (Defensive Perspective)
If you run a bug bounty program, protect your triage team from AI noise using WAF rules and automated de‑duplication.
Step‑by‑step guide for Cloudflare WAF:
Rate limit based on AI-generated payload signatures
Example: Block requests containing multiple encoded variations of common XSS
curl -X POST "https://api.cloudflare.com/client/v4/zones/ZONE_ID/rulesets" \
-H "Authorization: Bearer API_TOKEN" \
-H "Content-Type: application/json" \
--data '{
"rules": [{
"action": "block",
"expression": "(http.request.uri.query contains \"%3Cscript%3E\") or (http.request.uri.query contains \"javascript:alert\") or (http.request.uri.query matches \"onload=.alert\")"
}]
}'
Also implement smart de‑duplication with AI:
Use sentence transformers to cluster similar reports
from sentence_transformers import SentenceTransformer, util
model = SentenceTransformer('all-MiniLM-L6-v2')
reports = ["XSS at /search?q=test", "XSS found on search parameter", "SQLi in id param"]
embeddings = model.encode(reports)
Cluster and keep only unique findings
Why this matters: Programs that don’t adapt will drown like cURL did. AI‑powered filtering reduces false positives by 80%, allowing human triage to focus on critical vulnerabilities.
What Undercode Say:
- AI is not replacing hunters—it’s replacing slow hunters. Those who build custom agents are finding 5x more bugs, but the need for human creativity in business logic flaws remains.
- The spam crisis is a triage problem, not an AI problem. Programs that implement AI‑based filtering will survive; those that don’t will shut down. Hunters who submit unfiltered AI output will be banned.
- Local LLMs are the new must‑have tool. Cloud APIs are convenient but expensive and rate‑limited for large‑scale scanning. Ollama + llama3.2 gives you unlimited, private inference for $0.
Prediction:
Within 18 months, 60% of bug bounty programs will require automated AI triage as a submission prerequisite. Human hunters will shift exclusively to complex chained exploits and business logic flaws that current LLMs cannot reason about. Platforms like Intigriti and HackerOne will release official AI agent SDKs, and the top leaderboards will be dominated by hybrid human‑AI teams. The “vibe hacker” era is ending—the “agent orchestrator” era has begun.
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IT/Security Reporter URL:
Reported By: Nahamsec Ai – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
