attack-tree.md

Attack Tree Analysis for gabime/spdlog

Objective: To cause a denial-of-service (DoS) or gain unauthorized information disclosure via exploiting weaknesses or vulnerabilities within the application's use of the spdlog library.

Attack Tree Visualization

• Denial of Service (DoS)

  • Disk Space Exhaustion (File-Based Sinks)
    • Misconfigured File Rotation [CRITICAL]

• Information Disclosure

  • Sensitive Data Leakage in Logs
    • Application Logic [CRITICAL]

Attack Tree Path: Denial of Service (DoS) -> Disk Space Exhaustion (File-Based Sinks) -> Misconfigured File Rotation [CRITICAL]

• Description: This path focuses on causing a denial-of-service by filling up the disk space used for logging. The critical vulnerability lies in the misconfiguration of file rotation. If log files are not rotated (deleted or archived) properly, they can grow indefinitely, consuming all available disk space. This can lead to application crashes, system instability, and inability to write new data.
• Likelihood: High. Many applications are deployed with default or poorly configured logging settings. Developers often overlook the importance of log rotation until it becomes a problem. It's a common operational oversight.
• Impact: High. Complete disk exhaustion can cripple a system. Essential services may fail, and the system might become unresponsive, requiring manual intervention (and potentially data loss) to recover.
• Effort: Low. The attacker doesn't need to actively exploit a vulnerability in the code. They simply need to generate enough log data (which could be legitimate traffic or malicious, high-volume requests) to fill the disk. The misconfiguration is the vulnerability.
• Skill Level: Low. No specialized hacking skills are required. Understanding of how logging works and the ability to generate traffic (e.g., through repeated requests) is sufficient.
• Detection Difficulty: Medium. While disk space monitoring tools will eventually trigger alerts, they might not immediately pinpoint spdlog as the culprit. Log analysis would be needed to identify the rapidly growing log files. The delay in detection can allow significant damage to occur.

Attack Tree Path: Information Disclosure -> Sensitive Data Leakage in Logs -> Application Logic [CRITICAL]

• Description: This path represents the leakage of sensitive information (passwords, API keys, personal data, etc.) through log files. The critical node here is the application logic itself. The vulnerability isn't in spdlog directly, but in how the application uses it. If the application code inadvertently logs sensitive data, spdlog will dutifully record it.
• Likelihood: High. This is a very common mistake, especially in development environments where debugging information might be verbose. Developers might forget to remove or redact sensitive data before deploying to production. Even seemingly innocuous data can become sensitive when combined with other information.
• Impact: High to Critical. The impact depends entirely on the type of data leaked. Leaked credentials can lead to complete system compromise. Leaked personal data can lead to identity theft, financial loss, and reputational damage for both the user and the application provider. Compliance violations (GDPR, HIPAA, etc.) are also a major concern.
• Effort: Low. The attacker simply needs to gain access to the log files. This could be through a variety of means: misconfigured permissions, a separate vulnerability that allows file system access, social engineering, or even finding publicly exposed logs.
• Skill Level: Low. Reading log files and identifying sensitive information requires minimal technical skill.
• Detection Difficulty: Medium to High. Detecting this requires proactive log monitoring and analysis, ideally with tools that can identify patterns indicative of sensitive data (e.g., regular expressions for credit card numbers, API keys). Without such tools, it's easy for sensitive data to go unnoticed until a breach occurs.