CWE-362

Race conditions discovered during runtime testing where multiple threads or processes access shared resources without proper locking, leading to data corruption...

Verified by Precogs Threat Research
BASE SCORE
7.5 CRITICAL

Precogs AI Insight

"Precogs AI Binary DAST uses thread-aware fuzzing to trigger race conditions in multi-threaded compiled applications and firmware."

EXPLOIT PROBABILITYHigh
PUBLIC POCAvailable

What is CWE-362 (Concurrent Execution Using Shared Resource with Improper Synchronization (Race Condition))?

Race conditions discovered during runtime testing where multiple threads or processes access shared resources without proper locking, leading to data corruption.

Vulnerability Insights

In the context of binary ai-powered dast vulnerabilities, this vulnerability poses significant risk because compiled binaries and complex AI logic cannot be easily patched without vendor cooperation. Organizations relying on third-party software must use structural analysis tools to detect these flaws.

Impact on Systems

  • Memory Corruption: Crashing the daemon process
  • Execution Flow Hijacking: RCE via buffer overwrites

Real-World Attack Scenario

The attacker sends a carefully structured, oversized binary payload via the network interface. The vulnerable memory function processes the blob without checking size constraints, overwriting adjacent memory spaces or the instruction pointer. This allows the attacker to execute embedded shellcode or achieve a denial-of-service state.

Code Examples

Vulnerable Implementation

void process(char *input) {
    char buf[256];
    // VULNERABLE: Risky memory operations
    sprintf(buf, "Data: %s", input);
}

Secure Alternative

void process(char *input) {
    char buf[256];
    // SECURE: Bounds-checked operations
    snprintf(buf, sizeof(buf), "Data: %s", input);
}

Remediation

Ensure robust input validation, boundary checking, and adherence to secure architecture frameworks when designing Binary DAST solutions. Use automated code scanning or binary analysis to detect flaws early in the SDLC.