> For the complete documentation index, see [llms.txt](https://digitalgarden.batamladen.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://digitalgarden.batamladen.com/writeups/ctfs/ehax-ctf-2026/power-leak.md).

# power leak

**Category:** Forensics

***

### Challenge Description

> Power reveals the secret.\
> `EHAX{SHA256(secret)}`

We were given `power_traces.csv`.\
power\_traces.csv

***

### Understand the data

Opening the CSV revealed 5 columns:

```
position, guess, trace_num, sample, power_mW
```

* **position** — 6 positions (0–5), one per character of the secret
* **guess** — digits 0–9 being tested at each position
* **trace\_num** — 20 traces per guess
* **sample** — 50 power samples per trace
* **power\_mW** — the measured power consumption in milliwatts

This is a textbook **Differential Power Analysis (DPA)** setup. A device (e.g. a microcontroller checking a PIN) was measured while testing each digit guess at each position. The correct guess causes the device to do more work, which leaks as higher power consumption variance.

***

### Solution

The key insight: when the correct digit is tested, the device's power draw becomes more variable across traces compared to wrong guesses. So we simply find the guess with the **highest variance** at each position. Prompted chat for a quick script.

```python
import pandas as pd
import numpy as np
import hashlib

df = pd.read_csv('power_traces.csv')

secret = []
for pos in range(6):
    pos_df = df[df['position'] == pos]
    guess_vars = {}
    for g in range(10):
        g_df = pos_df[pos_df['guess'] == g]
        guess_vars[g] = g_df['power_mW'].var()
    best = max(guess_vars, key=guess_vars.get)
    secret.append(str(best))

secret_str = ''.join(secret)
flag = 'EHAX{' + hashlib.sha256(secret_str.encode()).hexdigest() + '}'
print(f'Secret: {secret_str}')
print(f'Flag: {flag}')
```

**Output:**

```
Position 0: guess 7  (var=69.06)
Position 1: guess 9  (var=68.97)
Position 2: guess 2  (var=68.21)
Position 3: guess 9  (var=69.28)
Position 4: guess 6  (var=67.24)
Position 5: guess 3  (var=67.65)

Secret: 792963
```

SHA256 the secret to get the flag format:

```
SHA256("792963") = 5bec84ad039e23fcd51d331e662e27be15542ca83fd8ef4d6c5e5a8ad614a54d
```

***

### Flag

```
EHAX{5bec84ad039e23fcd51d331e662e27be15542ca83fd8ef4d6c5e5a8ad614a54d}
```


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation by asking a question.

Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:

```
GET https://digitalgarden.batamladen.com/writeups/ctfs/ehax-ctf-2026/power-leak.md?ask=<question>&goal=<user_goal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.