Race Condition Hackviser May 2026

We loop the vulnerable binary thousands of times.

Target: Linux futex waiter list corruption (no published fix at time)

Vulnerability: Missing lock when walking futex_hash_bucket in futex_wake.

Hackviser approach (kernel module + userland):

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Rating: 8.5/10
🎯 Accuracy: Represents real-world concurrency bugs.
🧠 Didactic: Teaches defensive coding mindset.
⚡ Fun factor: Feels like a “magic trick” when you win twice the reward. race condition hackviser

Best for: Users comfortable with Python/Burp who want to move beyond basic SQLi/XSS.

Skip if: You dislike nondeterministic exploits or lack permission to run parallel requests.

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To reliably win the race (probability > 90%), the hackviser employs:

Mathematically, if each attempt wins with probability ( p = \frac\delta_\textattack\Delta t ), then after ( k ) attempts: We loop the vulnerable binary thousands of times

[ P_\textsuccess = 1 - (1-p)^k ]

The hackviser dynamically adjusts ( k ) until ( P_\textsuccess > 0.99 ).

A skilled hackviser focuses on three primary targets when exploiting race conditions:

As web applications become faster (HTTP/2, QUIC, WebSockets), race conditions are becoming easier, not harder. The modern "Hackviser 2.0" is now integrating AI to predict timing windows. By analyzing server response jitter (the standard deviation of response times), an AI model can predict precisely when a lock is released and schedule the next request to land in that 1ms gap. Rating: 8

The objective of this challenge is typically to read a sensitive file (like flag.txt or /etc/shadow) that is owned by root, but to which our low-privilege user does not have access.

The environment provides a SetUID (SUID) binary. This binary runs with the permissions of the file owner (usually root), but it is designed to only let us read files we already own.

Scenario:

Input: Target binary/endpoint, input vector
Output: Critical section location and ( \Delta t ) estimate

Techniques:

Example heuristic (Python pseudocode):

def estimate_race_window(endpoint, probes=1000):
    latencies = []
    for _ in range(probes):
        start = time.perf_counter_ns()
        response = concurrent_request(endpoint, threads=2)
        end = time.perf_counter_ns()
        if response.status == "collision":
            latencies.append(end - start)
    return np.percentile(latencies, 10)  # lower bound of race window