Full Report
This blog post is about the process we went through trying to better interpret the masses of scan results that automated vulnerability scanners and centralised logging systems produce. A good example of the value in getting actionable items out of this data is the recent Target compromise. Their scanning solutions detected the threat that lead to their compromise, but no humans intervened. It’s suspected that too many security alerts were being generated on a regular basis to act upon.
Analysis Summary
# Tool/Technique: Maltego (Used for Data Exploration and Visualization)
## Overview
Maltego is used in this context as a powerful data exploration and visualization tool designed to help security practitioners interpret masses of data generated by vulnerability scanners and centralized logging systems. The core purpose is to move beyond simple high-severity vulnerability listings towards actionable intelligence, such as correlating internal findings with external information like the existence of public exploits.
## Technical Details
- Type: Tool (Data Visualization/Exploration Framework)
- Platform: Desktop application (Transforms developed in Python for API interaction)
- Capabilities: Data correlation, graphical visualization (diagrams), custom transform development, integration with external APIs (CVE, Exploit-DB).
- First Seen: Not specified in the text, but the article was published in 2014.
## MITRE ATT&CK Mapping
The tool itself is an analytical/defensive tool, but its *application* here relates to analyzing adversary data sources and internal security posture. If we map the analysis of external data (like public exploits) against adversary activity:
- **TA0001 - Initial Access** (By correlating vulnerabilities that *can* be exploited externally)
- **T1190 - Exploit Public-Facing Application** (Understanding which internal assets have publicly exploitable high-risk vulnerabilities)
This mapping focuses on the *analysis* of the data gathered, not the execution of an attack.
## Functionality
### Core Capabilities
- **Data Sourcing:** Integrating data from Managed Vulnerability Scanning (MVS) APIs, CVE XML files, and Exploit-DB data.
- **Visualization:** Creating diagrams to represent relationships between clients, hosts, vulnerabilities, and the severity/exploitability associated with them.
- **Querying:** Allowing users to ask targeted questions about the data set (e.g., "Show me all hosts with a critical vulnerability in the last 30 days").
### Advanced Features
- **Custom Transforms:** Developers built specific transforms to query their custom CVE/Exploit-DB API and correlate this information with internal scanning results.
- **Maltego Machines:** Pre-packaged sequences of transforms designed to execute complex, multi-step data exploration tasks automatically.
- **Data Correlation:** Specifically designed to show vulnerabilities for which public exploit code exists, shifting focus to "vulnerabilities that matter."
- **Data Obfuscation:** Used a salted 'human readable hash' utility to obscure client names in demonstrations.
## Indicators of Compromise
This section is generally not applicable as Maltego is a defensive/analytical tool, not malware. The IoCs mentioned are derived from the data sources it processes:
- File Hashes: N/A
- File Names: N/A
- Registry Keys: N/A
- Network Indicators: N/A (The tool queries known external security data sources like NVD/Exploit-DB).
- Behavioral Indicators: N/A
## Associated Threat Actors
- **Defenders/Security Researchers/Consultancies** utilizing data analysis and visualization for vulnerability prioritization. (No specific threat actor group is associated with using Maltego for offensive purposes in this context).
## Detection Methods
Since this is an analysis tool, detection focuses on unauthorized usage or suspicious deployment:
- **Signature-based detection:** Signatures for the Python components or installed transforms (if available).
- **Behavioral detection:** Monitoring for the execution of Maltego client or automated transforms interacting with proprietary scanning APIs or scraping external CVE/Exploit data sources.
- **YARA rules:** Not applicable/specified.
## Mitigation Strategies
- **Prevention measures:** Strict access control and auditing for who can deploy and execute data analysis tools that aggregate vulnerability and asset data.
- **Hardening recommendations:** Ensure internal APIs (like the MVS API) use strong authentication and limit the rate of querying automated systems.
## Related Tools/Techniques
- Vulnerability Scanners (Nessus, Nmap, Netsparker) - Data sources feeding into Maltego.
- CVE XML databases (NVD).
- Exploit-DB.com data source.
- LogRhythm, Skybox (Future planned data sources).