Skip to content

Latest commit

 

History

History
109 lines (82 loc) · 10.2 KB

attack-tree.md

File metadata and controls

109 lines (82 loc) · 10.2 KB

Attack Tree Analysis for rsyslog/liblognorm

Objective: Compromise Application using liblognorm

Attack Tree Visualization

Attack Tree: High-Risk Paths & Critical Nodes - Compromise Application via liblognorm

Goal: Compromise Application using liblognorm

High-Risk Sub-Tree:

  • Compromise Application using liblognorm (CRITICAL NODE)
    • 1.0 Exploit Rulebase Vulnerabilities
      • 1.1 Malicious Rulebase Injection (CRITICAL NODE)
        • 1.1.1 Inject Malicious Rulebase File (HIGH-RISK PATH, CRITICAL NODE)
      • 1.2 Rulebase Parsing Vulnerabilities (CRITICAL NODE)
        • 1.2.1 Exploit Buffer Overflow in Rule Parser (CRITICAL NODE)
        • 1.2.3 Trigger Denial of Service via Complex/Malicious Rules (HIGH-RISK PATH, CRITICAL NODE)
    • 2.0 Exploit Log Input Vulnerabilities (CRITICAL NODE)
      • 2.1 Malicious Log Message Injection (CRITICAL NODE)
        • 2.1.1 Craft Log Message to Exploit Parsing Vulnerability (CRITICAL NODE)
          • 2.1.1.1 Buffer Overflow in Log Parsing (CRITICAL NODE)
          • 2.1.1.3 Injection via Unsanitized Log Data (HIGH-RISK PATH, CRITICAL NODE)
          • 2.1.1.4 Regular Expression Denial of Service (ReDoS) in Rule Matching (HIGH-RISK PATH, CRITICAL NODE)
      • 2.2 Denial of Service via Log Input (CRITICAL NODE)
        • 2.2.1 Send Large Volume of Logs to Overwhelm Parsing Resources (HIGH-RISK PATH, CRITICAL NODE)
        • 2.2.2 Send Log Messages that are Computationally Expensive to Parse (HIGH-RISK PATH, CRITICAL NODE)
    • 3.0 Exploit Logic/Design Flaws in liblognorm Usage
      • 3.2 Insecure Handling of Normalized Log Data (CRITICAL NODE)
        • 3.2.1 Storing Sensitive Data in Normalized Logs without Proper Encryption (HIGH-RISK PATH, CRITICAL NODE)

Attack Tree Path: 1. Compromise Application using liblognorm (Root Goal - CRITICAL NODE)

  • Attack Vector: This is the overarching goal. Success means an attacker has leveraged vulnerabilities in liblognorm or its usage to negatively impact the application's confidentiality, integrity, or availability.
    • Why High-Risk: Represents the ultimate failure from a security perspective.

Attack Tree Path: 2. 1.1.1 Inject Malicious Rulebase File (HIGH-RISK PATH, CRITICAL NODE)

  • Attack Vector: An attacker gains the ability to replace or inject a completely new rulebase file. This could be achieved through insecure file upload mechanisms, compromised administrative interfaces, or vulnerabilities in systems managing rulebase deployment.
    • Why High-Risk:
      • Full Control: Malicious rulebases can fundamentally alter log processing.
      • Data Manipulation: Logs can be dropped, falsified, or misclassified, hiding malicious activity or injecting false information.
      • Indirect Code Execution: While liblognorm might not directly execute code, malicious rules can be crafted to trigger vulnerabilities in downstream systems that process the normalized logs, especially if those systems naively trust the output.
      • Relatively Easy Effort: If rulebase loading is not properly secured, injection can be straightforward.

Attack Tree Path: 3. 1.2.1 Exploit Buffer Overflow in Rule Parser (CRITICAL NODE)

  • Attack Vector: Crafting a rulebase file with specific syntax or excessively long definitions that trigger a buffer overflow vulnerability in the liblognorm rule parser.
    • Why High-Risk:
      • Code Execution: Buffer overflows can lead to arbitrary code execution on the server running the application.
      • Full System Compromise: Successful code execution often results in complete system compromise.
      • High Impact, Lower Likelihood (but still critical): While exploiting buffer overflows requires technical skill and vulnerability research, the impact is so severe that it remains a critical risk.

Attack Tree Path: 4. 1.2.3 Trigger Denial of Service via Complex/Malicious Rules (HIGH-RISK PATH, CRITICAL NODE)

  • Attack Vector: Injecting or creating rulebase rules that are excessively complex or computationally expensive to parse and load. This can overwhelm the server's resources (CPU, memory) during rulebase loading.
    • Why High-Risk:
      • Denial of Service: Leads to application unavailability, disrupting normal operations.
      • Relatively Easy Effort: Crafting complex rules is often easier than exploiting memory corruption vulnerabilities.
      • Medium-High Likelihood: Poorly designed or unvalidated rulebases can easily introduce performance bottlenecks.

Attack Tree Path: 5. 2.1.1.1 Buffer Overflow in Log Parsing (CRITICAL NODE)

  • Attack Vector: Crafting malicious log messages, potentially very long or with specific formatting, to trigger a buffer overflow in the liblognorm log parsing logic.
    • Why High-Risk:
      • Code Execution: Similar to rulebase parser overflows, log parser overflows can lead to arbitrary code execution.
      • Full System Compromise: Code execution can result in complete system compromise.
      • High Impact, Lower Likelihood (but still critical): Exploiting buffer overflows is technically challenging, but the impact is critical.

Attack Tree Path: 6. 2.1.1.3 Injection via Unsanitized Log Data (HIGH-RISK PATH, CRITICAL NODE)

  • Attack Vector: Injecting malicious commands or code within log messages. If the application using liblognorm then processes the normalized log data without proper sanitization and uses it in security-sensitive operations (like command execution, SQL queries, etc.), injection vulnerabilities can be exploited.
    • Why High-Risk:
      • Command/SQL Injection: Can lead to arbitrary command execution on the server or unauthorized database access.
      • High Impact: Injection vulnerabilities are a major security concern, often leading to data breaches or system compromise.
      • Medium-High Likelihood (Application Dependent): The likelihood depends heavily on how the application uses the normalized log data. If not carefully handled, this is a significant risk.

Attack Tree Path: 7. 2.1.1.4 Regular Expression Denial of Service (ReDoS) in Rule Matching (HIGH-RISK PATH, CRITICAL NODE)

  • Attack Vector: Crafting log messages that are specifically designed to trigger Regular Expression Denial of Service (ReDoS) vulnerabilities in the regular expressions used within liblognorm rulebases for log matching.
    • Why High-Risk:
      • Denial of Service: ReDoS can cause excessive CPU consumption, leading to application unavailability.
      • Medium Likelihood: Regular expressions are commonly used in log parsing, and ReDoS vulnerabilities are a known issue if regex are not carefully designed and tested.

Attack Tree Path: 8. 2.2.1 Send Large Volume of Logs to Overwhelm Parsing Resources (HIGH-RISK PATH, CRITICAL NODE)

  • Attack Vector: Flooding the application with a massive volume of log messages. This can overwhelm liblognorm's parsing resources (CPU, memory, I/O), leading to denial of service.
    • Why High-Risk:
      • Denial of Service: Application becomes unavailable to legitimate users.
      • High Likelihood: DoS attacks by volume are relatively easy to execute.
      • Low Effort: Simple network tools can be used to generate high log volumes.

Attack Tree Path: 9. 2.2.2 Send Log Messages that are Computationally Expensive to Parse (HIGH-RISK PATH, CRITICAL NODE)

  • Attack Vector: Crafting specific log messages that, when processed by liblognorm rules, become computationally very expensive to parse. This can exhaust server resources and lead to denial of service, even with a lower volume of logs.
    • Why High-Risk:
      • Denial of Service: Application becomes unavailable.
      • Medium Likelihood: Requires some understanding of rulebase logic to craft expensive logs, but still achievable.
      • Medium Effort: Requires more effort than simple volume-based DoS, but still within reach of moderately skilled attackers.

Attack Tree Path: 10. 3.2.1 Storing Sensitive Data in Normalized Logs without Proper Encryption (HIGH-RISK PATH, CRITICAL NODE)

  • Attack Vector: If normalized logs contain sensitive data (credentials, personal information, API keys, etc.) and are stored without encryption, an attacker who gains unauthorized access to the log storage can compromise this sensitive data.
    • Why High-Risk:
      • Data Breach: Exposure of sensitive data can lead to significant financial, reputational, and legal damage.
      • High Impact: Data breaches are a major security incident.
      • Medium-High Likelihood (Application Dependent): Likelihood depends on whether sensitive data is logged and how securely logs are stored. Often, developers may overlook encryption for logs, making this a common vulnerability.