> For the complete documentation index, see [llms.txt](https://alham-rizvi.gitbook.io/alhamrizvi/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://alham-rizvi.gitbook.io/alhamrizvi/cryptography/hashing/43.-timing-attack-mac.md).

# 43. Timing Attack on MAC

## What is a Timing Attack?

A **timing attack** is a side-channel attack that exploits the time difference in operations to leak secret information.

For MAC verification, if comparison stops at the **first mismatched byte**, an attacker can guess the MAC byte-by-byte based on response time.

## Vulnerable Code Example

```python
# ❌ VULNERABLE — early exit on first mismatch
def verify(mac1, mac2):
    return mac1 == mac2   # Python str/bytes == is NOT constant-time
```

## Secure Code Example

```python
import hmac

# ✅ SECURE — constant-time comparison
def verify(mac1, mac2):
    return hmac.compare_digest(mac1, mac2)
```

## How the Attack Works

1. Send request with MAC = `00 00 00 ... 00`
2. Measure response time
3. Try `01 00 00 ... 00`, `02 00 00 ... 00`, etc.
4. When first byte is correct → response takes slightly longer
5. Repeat for each byte position

## CTF Exploitation Script (Skeleton)

```python
import requests
import time
import statistics

TARGET = "http://challenge.ctf/verify"
KNOWN = []

for pos in range(32):  # 32 bytes for SHA-256 MAC
    timings = {}
    for byte_val in range(256):
        candidate = KNOWN + [byte_val] + [0] * (31 - pos)
        mac = bytes(candidate).hex()
        times = []
        for _ in range(10):  # average over multiple requests
            start = time.perf_counter()
            requests.post(TARGET, json={"mac": mac})
            elapsed = time.perf_counter() - start
            times.append(elapsed)
        timings[byte_val] = statistics.mean(times)
    best = max(timings, key=timings.get)
    KNOWN.append(best)
    print(f"Byte {pos}: {best:02x}")

print("MAC:", bytes(KNOWN).hex())
```

## Notes

* Timing attacks are **statistical** — require many samples to be reliable
* Network jitter makes remote timing attacks harder (but not impossible)
* Tools like `dudect` analyze timing distributions statistically
* Often combined with oracle attacks in CTF challenges


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