Full Report
Potentially Unwanted Applications (PUAs) like NimScan are increasingly used by adversaries during the reconnaissance phase to map open ports or identify network assets. Detecting their execution early is key—but doing so with hash-based or path-based rules in Cortex XQL can result in logic that’s functional, but hard to interpret quickly. Uncoder AI’s AI-generated Decision Tree […] The post Detecting NimScan Execution with Uncoder AI’s Decision Tree for Cortex XQL appeared first on SOC Prime.
Analysis Summary
# Tool/Technique: NimScan Execution Detection
## Overview
This summary focuses on the methodology and tools used to detect the execution of the malware family **NimScan** using the **Uncoder AI Decision Tree** feature applied to **Cortex XQL** detection logic. The goal is to translate complex detection rules into clear, actionable decision trees for validation and incident response.
## Technical Details
- Type: Malware (NimScan) / Technique / Tool (Uncoder AI, Cortex XQL)
- Platform: Not explicitly detailed, but detection logic suggests endpoint/EDR telemetry processed by Cortex (implying Windows/Linux endpoints reporting to XDR/SIEM).
- Capabilities: NimScan's capabilities are the subject of detection; Uncoder AI's capability is translating complex query logic into decision trees.
- First Seen: Not specified in the provided context.
## MITRE ATT&CK Mapping
The context implies detection based on file characteristics (Path or IMPHASH), which maps broadly to detection techniques, although specific NimScan ATT&CK techniques were not enumerated in the snippet. A common mapping for malware execution detection based on artifacts is:
- **TA0005 - Defense Evasion**
- T1027 - Obfuscated Files or Information (If IMPHASH is used to bypass simple hash checks)
*Inferred Mapping based on execution artifacts:*
- **TA0002 - Execution**
- T1059 - Command and Scripting Interpreter (If NimScan utilizes scripting features)
## Functionality
### Core Capabilities
- **NimScan Detection**: Identifying the execution of NimScan based on artifacts like file path or IMPHASH within telemetry data.
- **Uncoder AI Decision Tree**: Converting dense detection logic (like Cortex XQL) into a visual, easy-to-understand decision tree format.
### Advanced Features
- **Operationalization**: Uncoder AI’s visualization streamlines the validation, tuning, and incident response process by making complex detection logic clear to engineers, threat hunters, and IR responders.
- **Logic Interpretation**: Facilitates the understanding of how specific artifacts (like IMPHASH) contribute to a high-fidelity alert for NimScan.
## Indicators of Compromise
The context specifies using the following types of input for detection logic:
- File Hashes: IMPHASH (Used as an identifier component in the detection logic)
- File Names: Not explicitly listed, but inferred via path analysis.
- Registry Keys: Not mentioned.
- Network Indicators: Not mentioned.
- Behavioral Indicators: Detection is triggered by execution artifacts (path/IMPHASH).
## Associated Threat Actors
- No specific threat actors are mentioned in the provided context snippet associated with NimScan usage of these detection methods.
## Detection Methods
- **Query Language**: Cortex XQL (Used as the input detection language).
- **Analysis Tool**: Uncoder AI Decision Tree (Used to visualize and operationalize the resulting detection logic).
- **Inputs Used**: File Path or IMPHASH.
## Mitigation Strategies
Since the context focuses on *detection*, mitigation strategies are implicitly related to preventing execution or identifying precursors:
- **Hardening**: Employing robust Endpoint Detection and Response (EDR) solutions capable of processing Cortex XQL logic.
- **Validation**: Using tools like Uncoder AI to ensure detection logic is high-fidelity and tuned, minimizing noise.
## Related Tools/Techniques
- **Cortex XQL**: The query language used to write the initial detection rule.
- **Uncoder AI**: The tool used for decision tree generation and visualization.
- **Detection as Code Platform**: Mentioned in the context of improving threat visibility, suggesting the overall environment where this detection operates.