Introduction

BITSCTF 2026 was a test of speed, structure, and technical depth. I competed as part of W4llz, where every solve required fast hypothesis testing, tooling precision, and zero room for sloppy reasoning.

From reconstructing encrypted data flows to dissecting malformed network traffic, this CTF demanded more than scripts. It demanded discipline. This writeup documents the challenges I tackled and the exploitation logic that led to each flag.

Let’s begin — starting with the first challenge.

Challenge 1: Jetpack Drift

The goal is to find a hidden flag by analyzing network traffic, decrypting data, and extracting information from an image.

1) Quick Look at the Network Traffic (PCAP Triage)

The first step is to examine the network traffic capture file, usually named chall.pcap. This file contains all the data that was sent and received during a specific network activity. We need to find interesting HTTP (Hypertext Transfer Protocol) requests and responses within this file.

To do this, we use a tool called tshark, which is a command-line network protocol analyzer. We can filter the traffic to show only HTTP requests and their details:

tshark -r chall.pcap -Y http \
 -T fields -e frame.number -e ip.src -e ip.dst \
 -e http.request.method -e http.host -e http.request.uri -e http.response.code

This command will list HTTP requests, showing:

• frame.number: The sequence number of the network packet.
• ip.src: The source IP address (who sent the request).
• ip.dst: The destination IP address (who received the request).
• http.request.method: The type of HTTP request (e.g., GET, POST).
• http.host: The website or server being accessed.
• http.request.uri: The specific path or resource being requested (e.g., /index.html, /api/data).
• http.response.code: The server’s response code (e.g., 200 OK, 404 Not Found).

By looking at the output, you should notice requests to several important files and endpoints:

• / (the main page)
• /manifest.json
• /robots.txt
• /encryption.py (likely contains encryption logic)
• /database.sql (a database dump, potentially containing sensitive information)
• /send-chunks.php (this is the most important one, as it handles sending data in chunks)

Next, we need to extract these HTTP objects (files) from the PCAP file. We can use tshark again to export them into a directory:

mkdir -p /tmp/pcap_http
tshark -r chall.pcap --export-objects http,/tmp/pcap_http
ls -l /tmp/pcap_http

This will:

1. Create a directory /tmp/pcap_http to store the extracted files.
2. Export all HTTP objects from chall.pcap into this directory.
3. List the contents of the /tmp/pcap_http directory, so you can see the extracted files.

Finally, we’ll examine the contents of the encryption.py and database.sql files, as they are crucial for understanding the encryption and finding the password:

sed -n '1,200p' /tmp/pcap_http/encryption.py
sed -n '1,200p' /tmp/pcap_http/database.sql

These commands use sed to display the first 200 lines of each file, allowing for a quick review of their content.

2) Understanding the Custom Encryption

By examining the encryption.py file, we can understand how the data was encrypted. Here’s a summary of the key points:

• Chunk Size: The data was broken into fixed-size pieces, each 12800 bytes long.
• Initial Key: The very first encryption key was generated by taking a password and applying the SHA-256 hashing algorithm to it (sha256(password)).
• Rolling Key: After encrypting one chunk, the key for the next chunk was generated by taking the unencrypted data (plaintext) of the current chunk and applying SHA-256 to it (sha256(plaintext_chunk)). This means the key changes for each chunk, depending on the previous chunk’s content.
• Chunk Header: Each encrypted chunk was sent with a special header. This header included NXTCHNKHASH:<sha256(encrypted_next_chunk)>DATA:<encrypted_chunk>. This NXTCHNKHASH acts like a link, pointing to the hash of the next encrypted chunk. The very last chunk had NXTCHNKHASH: followed by 64 zeros, indicating it was the end of the chain.

Based on this, to recover the original file, we need to:

1. Get the full HTTP response from the /send-chunks.php endpoint.
2. Decode the HTTP chunked transfer encoding, which is a standard way for web servers to send data in pieces.
3. Separate the custom chunk records, which include the NXTCHNKHASH and the encrypted data.
4. Use the NXTCHNKHASH values to put the chunks back in their correct order.
5. Decrypt each chunk one by one, using the rolling key mechanism.

3) Finding the Password

The database.sql file, which we extracted earlier, contains important information. While the main HTML page creates a username like “tyler” via JavaScript (which is irrelevant for decryption), the SQL dump reveals a real username and password hash combination worth examining.

Looking at the SQL insert format, here’s what stands out:

('tyler13bradley', 'lolagen31@pagesperso-orange.fr', '1VL7p6Rcli8mxgkh'),

From this entry, we can identify the password needed for the initial encryption key: 1VL7p6Rcli8mxgkh.

4) Extracting and Decrypting the Chunks (Exploit Script)

To automate the process of rebuilding, ordering, and decrypting the chunks, a Python script is used. This script performs the following actions:

1. Rebuilds the raw HTTP response: It reconstructs the complete HTTP response stream that contains the data from /send-chunks.php.
2. Decodes HTTP chunked transfer: It processes the standard HTTP chunked encoding to get the raw data chunks.
3. Parses custom chunk records: It extracts the NXTCHNKHASH and the encrypted data from each custom chunk record.
4. Orders chunks: It uses the NXTCHNKHASH values to link the chunks together and arrange them in the correct sequence. The last chunk is identified by a NXTCHNKHASH of 64 zeros.
5. Decrypts with rolling key: It decrypts each chunk using the AES-CTR algorithm, generating the next key from the plaintext of the current chunk.
6. Writes the recovered file: Finally, it combines all the decrypted plaintext chunks and saves them to a file.

Here’s the Python script (solve.py):

#!/usr/bin/env python3
import binascii
import hashlib
import subprocess
from Crypto.Cipher import AES
from Crypto.Util import Counter

PCAP = "chall.pcap"
PASSWORD = "1VL7p6Rcli8mxgkh"
OUTFILE = "/tmp/recovered_ordered.bin"

# 1) Get TCP stream index for /send-chunks.php
stream = subprocess.check_output([
    "tshark", "-r", PCAP,
    "-Y", 'http.request.uri=="/send-chunks.php"',
    "-T", "fields", "-e", "tcp.stream"
]).decode().strip()

if not stream:
    raise RuntimeError("Could not find TCP stream for /send-chunks.php")

# 2) Follow TCP stream raw
follow = subprocess.run([
    "tshark", "-r", PCAP, "-q",
    "-z", f"follow,tcp,raw,{stream}"
], capture_output=True, text=True).stdout.splitlines()

server_hex = [line.strip() for line in follow if line.startswith("\t")]
raw = binascii.unhexlify("".join(server_hex))

# 3) Split headers/body
if b"\r\n\r\n" not in raw:
    raise RuntimeError("Invalid HTTP response format")

headers, body = raw.split(b"\r\n\r\n", 1)

# 4) Decode HTTP chunked transfer
pos = 0
chunks = []

while True:
    line_end = body.find(b"\r\n", pos)
    if line_end == -1:
        break

    size_line = body[pos:line_end].strip().split(b";", 1)[0]
    size = int(size_line, 16)
    pos = line_end + 2

    if size == 0:
        break

    chunks.append(body[pos:pos + size])
    pos = pos + size + 2

body_dec = b"".join(chunks)

# 5) Parse custom chunk records
marker = b"NXTCHNKHASH:"
idx = 0
records = []

while idx < len(body_dec):
    if not body_dec.startswith(marker, idx):
        nxt = body_dec.find(marker, idx)
        if nxt == -1:
            break
        idx = nxt

    idx += len(marker)

    next_hash = body_dec[idx:idx + 64].decode("ascii")
    idx += 64

    if body_dec[idx:idx + 5] != b"DATA:":
        raise RuntimeError("Malformed record: missing DATA marker")

    idx += 5

    nxt = body_dec.find(marker, idx)
    if nxt == -1:
        enc = body_dec[idx:]
        idx = len(body_dec)
    else:
        enc = body_dec[idx:nxt]
        idx = nxt

    records.append((next_hash, enc))

if not records:
    raise RuntimeError("No records parsed")

# 6) Order chunks using forward hash chain
last = next((r for r in records if r[0] == "0" * 64), None)
if not last:
    raise RuntimeError("Could not find last chunk (zero hash)")

chain_rev = [last]
cur = last

while True:
    cur_hash = hashlib.sha256(cur[1]).hexdigest()
    prev = next((r for r in records if r[0] == cur_hash), None)
    if not prev:
        break
    chain_rev.append(prev)
    cur = prev

chain = list(reversed(chain_rev))

# 7) Decrypt in order
prev_key = hashlib.sha256(PASSWORD.encode()).digest()
plaintexts = []

for next_hash, enc in chain:
    nonce = prev_key[:16]
    ctr = Counter.new(128, initial_value=int.from_bytes(nonce, "big"))
    cipher = AES.new(prev_key, AES.MODE_CTR, counter=ctr)
    pt = cipher.decrypt(enc)
    plaintexts.append(pt)
    prev_key = hashlib.sha256(pt).digest()

all_plain = b"".join(plaintexts)

with open(OUTFILE, "wb") as f:
    f.write(all_plain)

print("Recovered", len(all_plain), "bytes ->", OUTFILE)

To run this script, save it as solve.py and execute it using Python 3:

python3 solve.py
file /tmp/recovered_ordered.bin

After running, the script will output the recovered file to /tmp/recovered_ordered.bin. The file command is then used to identify the type of this recovered file, which turns out to be an ICO file with some extra data.

5) Extracting the PNG from the ICO

The recovered file is an ICO (icon) file, but its header indicates that it contains an embedded PNG image. We need to extract this PNG image.

Here’s a Python script to do that:

import struct
from pathlib import Path

p = Path('/tmp/recovered_ordered.bin')
blob = p.read_bytes()

# ICO header structure: reserved, type, count
reserved, ico_type, count = struct.unpack('<HHH', blob[:6])
# Entry structure: width, height, color_count, reserved, planes, bitcount, size, offset
entry = blob[6:22]
width, height, color_count, reserved, planes, bitcount, size, offset = struct.unpack('<BBBBHHII', entry)

# Extract the PNG data using the size and offset found in the ICO header
png = blob[offset:offset+size]
Path('/tmp/extracted.png').write_bytes(png)
print('PNG saved to /tmp/extracted.png')

This script reads the ICO file, parses its header to find the size and starting position (offset) of the embedded PNG, and then extracts and saves that PNG data to /tmp/extracted.png.

6) Reading the Flag from the PNG (OCR)

The final step is to read the flag, which is text embedded within the extracted.png image. This requires Optical Character Recognition (OCR).

First, we use Python with the opencv library (cv2) to preprocess the image. This helps improve the accuracy of the OCR:

import cv2

img = cv2.imread('/tmp/extracted.png')
# Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Apply Gaussian blur to reduce noise
blur = cv2.GaussianBlur(gray, (5,5), 0)
# Detect edges using Canny algorithm
edges = cv2.Canny(blur, 50, 150)
# Find the coordinates of non-zero pixels (edges)
ys, xs = (edges > 0).nonzero()

# Crop the image tightly around the detected text
x0,x1 = xs.min(), xs.max()
y0,y1 = ys.min(), ys.max()
text = img[y0:y1+1, x0:x1+1]
cv2.imwrite('/tmp/text_crop.png', text)

# Convert the cropped text image to grayscale and then binarize it
# Binarization converts the image to pure black and white, which is good for OCR
text_gray = cv2.cvtColor(text, cv2.COLOR_BGR2GRAY)
_, th = cv2.threshold(text_gray, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
cv2.imwrite('/tmp/text_crop_th.png', th)

This script performs several image processing steps:

1. Reads the extracted.png image.
2. Converts it to grayscale.
3. Applies a Gaussian blur to smooth it out.
4. Uses the Canny edge detector to find the outlines of objects (like text).
5. Crops the image to focus only on the area where text is detected.
6. Binarizes the cropped image (converts it to pure black and white) to make the text clearer for OCR.

The preprocessed image (/tmp/text_crop_th.png) is then fed into tesseract, a powerful OCR engine:

tesseract /tmp/text_crop_th.png /tmp/ocr -l eng --psm 7 \
 -c tessedit_char_whitelist=ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789{}_

This tesseract command:

• Takes /tmp/text_crop_th.png as input.
• Outputs the recognized text to /tmp/ocr.txt.
• -l eng: Specifies that the language is English.
• --psm 7: Sets the page segmentation mode to treat the image as a single line of text, which is often good for flags.
• -c tessedit_char_whitelist=...: Restricts the characters Tesseract will look for, helping to reduce errors and focus on characters typically found in CTF flags.

Finally, you can view the extracted flag:

cat /tmp/ocr.txt

Expected OCR Output:

BITSCTF{LF1_st4nds_4_1ost_frOm_1ns1d3}

7) Final Flag

The flag obtained from the OCR process is:

BITSCTF{LF1_st4nds_4_1ost_frOm_1ns1d3}

With the flag confirmed, let’s move on to the next challenge.

Challenge 2: Radio Telescope

Challenge Overview

The challenge provided a single log file (rt7-log.txt) containing 10,001 lines of floating-point numbers recorded from a simulated radio telescope feed. The flavour text hinted: “the feed randomly skips a beat: a clearing in a forest.”

This is a steganographic signal analysis challenge. The flag is not hidden in a file header, a code comment, or an encrypted stream — it is embedded as short stable regions (clearings) inside a stream of pseudo-random noise. Each clearing encodes a single ASCII character, and together they spell the flag.

The attack surface is pure maths: statistical detection of low-variance windows within a high-variance dataset.

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Analysis Process

1. Initial Data Inspection

The log file contains 10,001 lines of floating-point numbers in scientific notation (e.g., 1.227758032072523804e+02). A quick statistical survey reveals:

• Range: approximately −62 to +213
• Mean: ≈ 75 (centred roughly in an ASCII printable range)
• Distribution: mostly chaotic — no obvious repeating pattern at first glance
• Outliers: short runs where values cluster tightly around an integer

That last observation is the key. Real radio telescope noise does not magically settle to a near-constant value for 20 consecutive samples. Wherever it does, something was inserted deliberately.

2. Identifying Anomalies

By inspecting the data, I looked for “clearings” — segments where the signal becomes stable. The key insight from the challenge hint (“a clearing in a forest”) is that hidden within the noise are short windows where consecutive values stabilize around a specific integer. That integer, when interpreted as an ASCII code, yields a character of the flag.

I wrote a Python script to detect these windows by sliding over the data and checking for runs with very low variance and proximity to a whole number:

#!/usr/bin/env python3
import statistics

WINDOW = 20       # samples per clearing
VARIANCE = 2.0    # max allowed variance within a window
MAX_FRAC = 0.2    # max fractional part distance from integer

def load_data(path):
    with open(path) as f:
        return [float(line.strip()) for line in f if line.strip()]

def find_clearings(data, window=WINDOW, max_var=VARIANCE, max_frac=MAX_FRAC):
    clearings = []
    i = 0
    while i <= len(data) - window:
        chunk = data[i:i + window]
        var = statistics.variance(chunk)
        if var < max_var:
            mean = statistics.mean(chunk)
            frac = abs(mean - round(mean))
            if frac < max_frac:
                clearings.append((i, round(mean)))
                i += window   # skip past this window to avoid overlapping detections
                continue
        i += 1
    return clearings

def main():
    data = load_data("rt7-log.txt")
    clearings = find_clearings(data)

    flag_chars = []
    print(f"{'Index':<8} {'Rounded':<10} {'ASCII'}")
    print("-" * 30)
    for idx, val in clearings:
        ch = chr(val) if 32 <= val <= 126 else '?'
        flag_chars.append(ch)
        print(f"{idx:<8} {val:<10} {ch}")

    flag = "".join(flag_chars)
    print(f"\nRaw extracted string : {flag}")
    # Wrap in BITSCTF format if not already present
    if not flag.startswith("BITSCTF{"):
        print(f"Flag                 : BITSCTF{{{flag[flag.index('{')+1:flag.index('}')]}}}")

if __name__ == "__main__":
    main()

Run it with:

python3 solve_telescope.py

3. Extraction

The script identified 25 distinct “clearings” occurring at regular intervals (approximately every 384 samples). Each clearing consists of 20 consecutive samples that are very close to a specific integer value.

Index	Rounded Value	ASCII Character
374	67	C
758	84	T
1142	70	F
1526	123	{
1910	115	s
2294	49	1
2678	108	l
3062	51	3
3446	110	n
3830	99	c
4214	51	3
4598	95	_
4982	49	1
5366	110	n
5750	95	_
6134	116	t
6518	104	h
6902	51	3
7286	95	_
7670	110	n
8054	48	0
8438	49	1
8822	115	s
9206	51	3
9590	125	}

Final Flag

The extracted string is CTF{s1l3nc3_1n_th3_n01s3}. Given the required format BITSCTF{}, the flag is:

BITSCTF{s1l3nc3_1n_th3_n01s3}

And there’s the flag.

Conclusion

BITSCTF 2026 covered a satisfying breadth of disciplines — from classical network forensics and custom encryption reversal in Jetpack Drift, to pure statistical signal analysis in Radio Telescope. Both challenges rewarded deliberate methodology over brute force.

Key takeaways:

• PCAP analysis with tshark remains one of the fastest ways to reconstruct encrypted data flows. Knowing how to follow TCP streams in raw hex, decode chunked transfer encoding, and then layer a custom protocol on top is an essential skill.
• AES-CTR with a rolling key is a common pattern in CTF challenges. Because CTR mode turns AES into a stream cipher (XOR of keystream and plaintext), decryption is identical to encryption — and the key schedule here makes each block dependent on the previous plaintext, meaning you must proceed in order.
• Statistical steganography (hiding data in noise variance) is subtle but detectable with a simple sliding-window approach. When the challenge flavour text says “a clearing in a forest”, it is almost always a hint about a region of low entropy in a high-entropy dataset.
• Clean, reproducible scripts matter. Every script in this writeup can be dropped into a fresh environment and will reproduce the exact results shown.

Competing as part of W4llz pushed us to move faster, think cleaner, and eliminate hesitation. Every CTF exposes new gaps — BITSCTF 2026 was no different, and every gap is an opportunity.

HAPPY HACKING!