Description
You intercepted ABC School's token generator. It seeds Python's Mersenne Twister with int(time.time()*1000) and gives you 50 guesses. Synchronize on the server time, brute-force the nearby millisecond range, and submit the matching token.
Study token_generator.py to learn the alphabet, seed, and token length (20 characters).
Connect to nc verbal-sleep.picoctf.net <PORT_FROM_INSTANCE> and synchronize with the server time when sending guesses.
pip install pwntoolspython3 script.py # see snippet belowSolution
Walk me through it- Step 1Replicate get_randomReimplement the provided
get_random()locally but accept a custom time argument. Seed withint(t*1000)to mimic the challenge. MT19937 is deterministic: same seed produces the exact same sequence, so replicating get_random with the right seed produces an identical token.Learn more
Python's built-in
randommodule uses the Mersenne Twister algorithm (MT19937), a pseudo-random number generator (PRNG) designed for statistical quality, not cryptographic security. Its critical property here is that it is deterministic: given the same seed, it will always produce the exact same sequence of numbers. This means anyone who knows the seed can reproduce all past and future outputs.When the seed is derived from
time.time()- the current Unix timestamp - the seed space is severely limited. Using millisecond precision (int(time.time() * 1000)) gives only about 1,000 possible seeds per second. An attacker who can estimate when the token was generated (for example, by measuring round-trip time to the server) can enumerate all plausible seeds in a fraction of a second.The correct approach for token generation is to use cryptographically secure random number generators:
secrets.token_hex()oros.urandom()in Python, which draw from the operating system's entropy pool (e.g.,/dev/urandomon Linux). These are non-deterministic and cannot be predicted even with knowledge of the current time. - Step 2Brute-force nearby timestampsRecord
t0 = int(time.time() * 1000)at connect time and assume the server seeded within ±25 ms of t0 (network latency under 50 ms). Iterate that centered window. For each candidate ms, reseed locally, generate the 20-char token, and submit. Typical RTT is 10-100 ms, so ±25 ms covers most internet latencies; if no hit, network jitter or server clock skew shifted the range, so re-run.pythonfrom pwn import * import random, time def get_random(length, t_ms): alphabet = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz" random.seed(t_ms) return "".join(random.choice(alphabet) for _ in range(length)) p = remote("verbal-sleep.picoctf.net", 64704) base_ms = int(time.time() * 1000) for offset in range(-25, 25): # 50 guesses centered on now candidate_ms = base_ms + offset guess = get_random(20, candidate_ms) p.recvuntil(b": ") # prompt ends with ": " (defensive) p.sendline(guess.encode()) line = p.recvline() if b"Congratulations" in line or b"picoCTF{" in line: print(line.decode(errors="replace")) print(p.recvall(timeout=2).decode(errors="replace")) break else: print("[!] No hit. Re-run; network jitter may have shifted the window.")Learn more
Pwntools is a Python library built for CTF exploit development and binary exploitation. Its
remote()function creates a socket connection and exposes convenient methods likerecvuntil()(block until a specific byte sequence is received),sendline()(send data followed by a newline), andinteractive()(hand the connection to the user for manual interaction). This allows complex multi-round protocol interactions to be scripted precisely.The attack works because network latency introduces only a small, bounded clock difference between client and server. By recording the client's timestamp at connection time and scanning a small window of milliseconds around it, the attacker covers the range of seeds the server could have used. With 50 guesses allowed and a roughly 50 ms window to search, the probability of success is very high.
This class of vulnerability is called a time-based seed attack or temporal PRNG attack. Real-world examples include PHP's early
mt_rand(time())usage for session tokens, early versions of WordPress generating password reset tokens from the current time, and various Java applications usingnew Random(System.currentTimeMillis()). All were vulnerable to the same prediction technique demonstrated here. The pwntools for CTF guide covers the recvuntil/sendline patterns used in this exploit, and Python for CTF covers the random/secrets module distinctions.
Flag
picoCTF{UseSecure#$_Random@j3n3r@T0rsd...}
Network jitter means you may need to rerun the script, but scanning a 50 ms window is enough.
How to prevent this
How to prevent this
Seeding a PRNG with the current time gives you ~1 second of entropy. The flag literally says use a secure RNG.
- Use a CSPRNG:
secrets.token_bytes()in Python,crypto.randomBytes()in Node,/dev/urandomin shell,rand::thread_rng()in Rust. Neverrandom.seed(time())for anything security-relevant. - For tokens, IDs, and keys, generate at least 128 bits of entropy.
secrets.token_urlsafe(32)gives 256 bits and is shareable in URLs without escaping. - Audit your code for
Math.random(),random.random(),rand(), and any time-based seeding. Linters (Bandit B311, ESLint security plugin) flag these automatically.