CWE-78

Runtime detection of command injection in compiled applications where user input is passed to system() or exec() without sanitization....

Verified by Precogs Threat Research
BASE SCORE
7.5 CRITICAL

Precogs AI Insight

"Precogs AI injects command injection payloads during dynamic testing and monitors OS-level process creation to confirm exploitability."

EXPLOIT PROBABILITYHigh
PUBLIC POCAvailable

What is CWE-78 (Improper Neutralization of Special Elements used in an OS Command (OS Command Injection))?

Runtime detection of command injection in compiled applications where user input is passed to system() or exec() without sanitization.

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

  • Remote Code Execution (RCE): Full control over the host operating system
  • Lateral Movement: Pivoting from the compromised server into the internal network
  • Data Exfiltration: Stealing files, environment variables, or secrets from the host

Real-World Attack Scenario

An attacker identifies a diagnostic endpoint that accepts an IP address to ping. Instead of a valid IP, they submit 127.0.0.1; cat /etc/passwd. Because the application concatenates this input directly into a system shell command, the shell executes the ping and then successfully executes the attacker's injected command, revealing sensitive host files.

Code Examples

Vulnerable Implementation

import os
target = request.args.get('ip')
# VULNERABLE: Unsanitized input passed to system command
os.system(f"ping -c 4 {target}")

Secure Alternative

import subprocess
target = request.args.get('ip')
# SECURE: Input validated, shell=False prevents injection
if is_valid_ip(target):
    subprocess.run(["ping", "-c", "4", target], shell=False)

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.