Race Conditions – Exploiting Timing Gaps in Concurrent Operations

Table of Contents

1. Introduction: The Invisible Time Bomb
2. Concurrency 101: Threads, Processes & Shared Resources
3. Anatomy of a Race Condition
   • 3.1 Critical Sections & Data Races
   • 3.2 TOCTOU (Time-of-Check to Time-of-Use)
   • 3.3 Heisenbugs: Why Race Conditions Evade Detection
4. Real-World Exploits: When Timing is Everything
   • 4.1 Therac-25: The Killer Machine
   • 4.2 Dirty COW: Linux Privilege Escalation (CVE-2016-5195)
   • 4.3 E-commerce Inventory Overselling
   • 4.4 Financial System Double-Spending Attacks
5. Exploitation Techniques
   • 5.1 Symbolic Link Attacks (File Systems)
   • 5.2 Web API Token Hijacking
   • 5.3 Hardware-Level Rowhammer Exploits
6. Prevention & Mitigation Strategies
   • 6.1 Mutexes, Semaphores & Spinlocks
   • 6.2 Atomic Operations & Immutable Data
   • 6.3 Lock-Free Algorithms (e.g., RCU)
   • 6.4 Transactional Memory (Hardware/Software)
7. Testing for Race Conditions
   • 7.1 Fuzzing with Thread Sanitizers
   • 7.2 Formal Verification (TLA+, SPIN)
   • 7.3 Chaos Engineering (Jepsen, Chaos Monkey)
8. Case Study: Exploiting a Voting System
9. Future Threats: Quantum Computing & Beyond
10. Conclusion: Designing Race-Free Systems

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1. Introduction: The Invisible Time Bomb

“Race conditions turn nanoseconds into nightmares.”
Imagine two sprinters racing toward a finish line—but what if the track vanishes mid-stride? In software, race conditions occur when concurrent operations (threads, processes, or distributed nodes) access shared resources in unpredictable sequences, creating catastrophic timing gaps. These gaps are exploited to corrupt data, escalate privileges, or crash systems. This blog dissects how attackers weaponize these microscopic windows of chaos and how to defend against them.

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2. Concurrency 101: Threads, Processes & Shared Resources

Parallelism vs. Concurrency

• Parallelism: Simultaneous execution (multi-core).
• Concurrency: Logical simultaneity (single-core with context switching).

Shared Resources: The Battlefield

• Memory locations, files, databases, network sockets.
• Problem: Unsynchronized access → data races.

Code Snippet: The Classic Bank Account Race

python

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balance = 100  
def withdraw(amount):  
    global balance  
    if balance >= amount:  
        # Timing gap allows another thread to run here!  
        balance -= amount  

Two threads withdrawing $60 each can overdraw the account.

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3. Anatomy of a Race Condition

3.1 Critical Sections & Data Races

• Critical Section: Code segment accessing shared resources.
• Data Race: Concurrent unsynchronized access to mutable state.

3.2 TOCTOU (Time-of-Check to Time-of-Use)

c

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if (access("/tmp/userfile", W_OK)) {  // CHECK  
    // Attacker replaces /tmp/userfile with a symlink here!  
    fd = open("/tmp/userfile", O_WRITE);  // USE  
    write(fd, buffer, sizeof(buffer));  
}  

Exploit: Symlink to /etc/passwd → Privilege escalation.

3.3 Heisenbugs: Why Race Conditions Evade Detection

• Non-deterministic (depends on CPU scheduler, load, luck).
• Disappear under debuggers (timing changes).

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4. Real-World Exploits

4.1 Therac-25: The Killer Machine (1985)

• Flaw: Race between keyboard input and machine state.
• Result: Radiation overdoses killing patients.

4.2 Dirty COW (CVE-2016-5195)

• Vulnerability: Race in Linux kernel’s memory subsystem.
• Exploit: Write to read-only memory → Root access.

4.3 E-commerce Inventory Overselling

• Scenario: 100 laptops in stock.
• Race: Two purchases read “100” simultaneously → Both sell item #100.

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5. Exploitation Techniques

5.1 Symbolic Link Attacks

bash

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# Attacker's script:  
while true; do  
  ln -sf /etc/passwd /tmp/target;  
  ln -sf /tmp/attacker_file /tmp/target;  
done  

Race to swap a file between check and use.

5.2 Web API Token Hijacking

• Flaw: OAuth token re-use race.
• Attack: Two sessions race to consume the same token → Account takeover.

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6. Prevention & Mitigation Strategies

6.1 Synchronization Primitives

• Mutexes:pythonCopyDownloadfrom threading import Lock lock = Lock() def safe_withdraw(amount): with lock: if balance >= amount: balance -= amount
• Semaphores: Limit concurrent access (e.g., database connections).

6.2 Lock-Free Programming

• Atomic operations: compare_and_swap(), fetch_and_add().
• Example: Atomic increment in Java:javaCopyDownloadAtomicInteger balance = new AtomicInteger(100); balance.getAndAdd(-50); // Thread-safe

6.3 Architectural Defenses

• Immutable data: Event sourcing, functional programming.
• Distributed locks: Redis Redlock, ZooKeeper.

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7. Testing for Race Conditions

7.1 Thread Sanitizers (TSan)

bash

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# Compile with Clang's TSan:  
clang -fsanitize=thread -g race.c && ./a.out  

Detects data races at runtime.

7.2 Chaos Engineering

• Netflix Jepsen: Simulates network partitions.
• Chaos Monkey: Randomly kills production servers.

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8. Case Study: Exploiting a Voting System

• Flaw: Race between vote tallying and ballot submission.
• Attack:
  1. Submit 100 votes in parallel.
  2. Vote counter reads partial state → Tallies 150 votes from 100 ballots.
• Fix: Database transactions with SERIALIZABLE isolation.

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9. Future Threats: Quantum Computing & Beyond

• Quantum race conditions: Superposition of states enabling new attack vectors.
• Mitigation: Quantum-resistant locks via hardware-enforced synchronization.

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10. Conclusion: Designing Race-Free Systems

“Concurrency is not about speed—it’s about correctness.”

• Golden Rules:
  1. Minimize shared mutable state.
  2. Prefer immutability and pure functions.
  3. Test concurrency under realistic chaos.
• Final Thought: In the arms race of microseconds, the defender’s clock never stops.

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