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Fuzzing semantic similarity models and timestamps against historical/CTF timeline dates.

Tools Used

python-requestsSemantic Fuzzing

Overview

The challenge provides a predict endpoint that compares inputs with a hidden target sequence based on semantic similarity.

Reconnaissance

Fuzzing time-related phrases yields score boosts. Testing the phrase time wheel reaches a 100% semantic score of 1.0.

Exploitation Strategy

However, a secondary temporal check is active on the timestamp parameter. Fuzzing timestamps against the CTF start date (2026-03-28) triggers the flag response.

Solution Code

import requests

r = requests.post("http://138.199.163.92:10675/predict", json={
    "text": "time wheel",
    "timestamp": "2026-03-28"
})
print(r.json())

Flag

Kaal{Tareekh_pe_tareekh}