Captcha Me If You Can Root Me

To keep your progress and the CAPTCHA valid, you must use a persistent session. In Python, this is best handled with the requests.Session() 2. Download the Image

Converts the image strictly to black and white pixels, turning the background pure white and the text solid black.

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71 lines (51 loc) · 2.28 KB. Raw. Copy raw file. Download raw file. Open symbols panel. Edit and raw actions. import requests, re, CAPTCHA Me if You Can! - /dev/random

Converting the image to black and white (thresholding) to isolate characters. captcha me if you can root me

from PIL import Image

return "Validé" in response.text # Root-Me success indicator

def fetch_captcha_image(self, img_url): response = self.session.get(img_url) return Image.open(io.BytesIO(response.content))

Solving this challenge requires a pipeline that mimics a human's sensory and cognitive steps, compressed into milliseconds. To keep your progress and the CAPTCHA valid,

Clean backgrounds lack programmatic interference like grid lines, color gradients, or random dots.

Every time you request a CAPTCHA, the server associates that specific image with your current session ID via a cookie. If you download the image with one script and try to submit the answer with another independent request, the server will treat them as two different sessions. You will fail.

Tesseract OCR scans the cleaned image to convert the visual text into a string variable. Because these CAPTCHAs are short, you should configure Tesseract's Page Segmentation Mode (PSM) to treat the image as a single word or a single line of text. 4. Automated Submission

import requests import pytesseract from bs4 import BeautifulSoup from io import BytesIO # Configuration for Tesseract path if required by your OS # pytesseract.pytesseract.tesseract_cmd = r'/usr/bin/tesseract' def solve_challenge(target_url, submit_url): # Initialize a session to automatically persist cookies session = requests.Session() # 1. Fetch the challenge landing page response = session.get(target_url) soup = BeautifulSoup(response.text, 'html.parser') # 2. Extract image location (Assuming base64 or source link format) img_element = soup.find('img') img_src = img_element['src'] # Download the raw image data img_response = session.get(img_src) img = Image.open(BytesIO(img_response.content)) # 3. Clean and process image (Utilizing logic from Phase A) # processed_img = clean_captcha_image(img) # 4. Extract text via OCR # config flag '--psm 8' tells Tesseract to treat the image as a single word extracted_text = pytesseract.image_to_string(img, config='--psm 8').strip() # 5. Post the answer back to the server payload = 'captcha_field_name': extracted_text result = session.post(submit_url, data=payload) if "Flag" in result.text or "Success" in result.text: print(f"Success! Extracted text: extracted_text") print(result.text) # Display your reward/flag else: print(f"Failed attempt. OCR read: extracted_text. Trying again...") Use code with caution. 🛡️ Mitigations: How Modern Systems Defend Themselves Copy raw file

While CAPTCHAs ("Completely Automated Public Turing test to distinguish between Computers and Humans") were originally designed to block malicious bots, this classic challenge demonstrates how easily standard visual protections fail when confronted with targeted programming. 🧭 The Core Mechanics of the Challenge

The CAPTCHA is validated entirely in the browser using JavaScript, allowing an attacker to intercept and drop the validation requirement via a proxy like Burp Suite.

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