XRefer: HTML Exporter and Visualizer for XRefer's Cluster Analysis

[DeshDeepakKant]

This is the place to discuss the XRefer: HTML Exporter and Visualizer for Cluster Analysis project for Google Summer of Code 2025.

XRefer: HTML Exporter and Visualizer for XRefer's Cluster Analysis

size: medium, estimated 160 hours

difficulty: low

mentors: @m-umairx

The goal of this project is to design and implement an HTML export module for XRefer. The module will convert XRefer's internal cluster analysis data into a dynamic HTML visualization. This interactive output should allow users to:

• View Cluster Graphs: Render detailed graphs illustrating the relationships between clusters.
• Read Semantic Descriptions: Provide natural language explanations for each cluster and its contained functions.
• Interact with Data: Offer interactive controls (e.g., zoom, pan, node selection, filtering) to explore and analyze clusters in depth.

Deliverables:

• Design and Architecture
  • Develop an intuitive UI/UX design that outlines how clusters and their semantic descriptions will be presented. Consider interactive elements such as zoomable graphs, clickable nodes, and filtering options.
  • Evaluate and choose suitable front-end libraries or frameworks (e.g., D3.js, Cytoscape.js) for rendering graphs and managing interactivity.
• Develop the HTML Export Module
  • Create a Python module to convert XRefer's cluster analysis data into a format consumable by the front-end (e.g., JSON).
  • Develop a responsive HTML template that integrates the chosen visualization libraries. The template should include placeholders for cluster graphs, semantic descriptions, and interactive controls.
  • Implement features such as zoom, pan, node highlighting, and tooltips to enhance the user's exploratory experience.
  • Integrate the export module into the existing XRefer workflow so that a .html file is generated as part of the analysis process.
• Documentation
  • Document the design decisions, data transformation process, and integration steps to help future contributors extend or maintain the module.

Required Skills

• Proficiency in Python programming language.
• Familiarity with HTML, CSS, and JavaScript for building interactive web interfaces.
• Experience with visualization libraries (e.g., D3.js, Cytoscape.js) or willingness to learn how to implement interactive graphs.
• Ability to conceptualize and design an intuitive user interface that effectively presents complex data.

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To ensure the success of this project, we highly value the guidance and feedback of @m-umairx, our mentor for this initiative. Your expertise in architecture, implementation, and best practices will be instrumental in shaping the project effectively and ensuring its successful execution.

We also encourage contributors interested in this project to actively participate by sharing their insights, asking questions, and suggesting improvements in this discussion.

[DeshDeepakKant]

Hi @m-umairx,

I am Desh Deepak Kant, a 3rd-year BTech Computer Science and Engineering student at IIIT Agartala. I am passionate about Open Source Contribution and truly enjoy working on innovative projects.

I am interested in contributing to this project.

Regarding my experience and skills:

I am a Linux Foundation Mentee (LFX 2025 Term-1) 🔗 Profile Link
Currently, I am working on a CNCF project – The Update Framework (TUF) 🔗 Project Link

My contributions involve developing a JSON visualization tool using D3.js for TUF metadata repositories, enabling easy auditing and review through a deployable web interface. This project aligns closely with the XRefer HTML Exporter and Visualizer in terms of data transformation, visualization, and UI interactivity.

I have been contributing to multiple CNCF Open Source Projects, and regarding the required skills:

Proficient in Python for backend processing.
Experienced in HTML, CSS, and JavaScript for building interactive web interfaces.
Worked with D3.js for creating dynamic data visualizations.
Strong understanding of Git and GitHub workflows.

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Regarding this project:

As mentioned in the Application Steps:

"Prepare a patch related to the existing FLARE project to practice interacting with us on GitHub."

However, the XRefer repository currently has no open issues 🔗 Issue Tracker

I would like to know what the pretest or pretask for this project would be. Should we directly start discussing the approach for implementation?

For now, I am actively exploring the XRefer codebase and planning a structured approach for this project. Looking forward to your guidance and discussions!

[m-umairx]

Hi @DeshDeepakKant,

Great to hear that you've worked with data visualization before! As you noticed, there aren't any minor bug fixes open for XRefer at the moment. I could assign some tasks from the roadmap, but that might take us off track from our current focus.

Do you have access to a licensed copy of IDA? That would be ideal, but if not, this task is independent enough that we can find a workaround.

To start, I think it's important to get a clear understanding of the data we intend to visualize. Have you had a chance to look into it yet? If so, I'd love to hear what you've learned so far. Let me know if you have any questions as well!

Looking forward to your thoughts.

[DeshDeepakKant]

I don't have access to a licensed copy of IDA,

The project idea is very well explained, and your documentation is clear. I’m able to relate the different aspects and understand the overall concept effectively.

I've analyzed the data structure,

1. Cluster Information : Each cluster is identified by a unique ID, a name, and a description that explains its role. Clusters also contain lists of functions, which include their names and addresses.

2. Cluster Relationships : Clusters interact through defined relationships, such as function calls. Each relationship has a specific type, like "calls," along with a description providing context.

3. Binary Description : The data includes a category, such as "Ransomware," along with a high-level summary of its functionality.

What would be the next step to take? Also, how can I get access to XRefer's cluster analysis data?

[m-umairx]

Given that you don't have access to an IDA license, I think we need to prioritize the following task so that you can work on the visualization code independently of XRefer:

Create a Python module to convert XRefer's cluster analysis data into a format consumable by the front-end (e.g., JSON)

Your understanding of the data structure is correct. Additionally, it's important to understand that each functional cluster can have its own child clusters, and the same child cluster can be a child of more than one cluster. We can also have independent sets of clusters where there is no link between 2 sets of clusters.

Immediate Next Steps:

1. Review and understand the cluster data structure

   • Examine the code at: https://github.com/mandiant/xrefer/blob/main/plugins/xrefer/core/clusters.py
   • Examine any additional relevant code based on your current understanding

2. Propose a data structure format

   • Based on your understanding, propose an appropriate format to store this data independently. Maybe JSON? or something else? This would form the basis of all our visualizations later on. Once you propose a skeleton of the storage structure and identify where to extract this data from within XRefer, we can discuss whether it will meet our requirements and modify it accordingly.

3. Identify data extraction points

   • Suggest where in the XRefer codebase this data would need to be extracted from
   • Consider how we might transform the internal representation to your proposed format

Please note that we are still in the discussion phase. We want to finalize the data structure design before writing any code. Since you don't have IDA access, I can help test your data export code when you begin implementation. Once the data export is implemented, I will be able to provide you with test data that you can use to conceptualize the visualizations and work on subsequent tasks independent of IDA. This data structure design can also serve as your initial contribution to the project in place of the patch you were looking for.

Once we've finalized the data structure through discussion, you can:

1. Create an issue to track this effort
2. Fork the repository
3. Work on implementing the data exporter
4. Submit a PR to the gsoc_2025 branch (https://github.com/mandiant/xrefer/tree/gsoc_2025)

Please feel free to let me know when you have any questions, or feel like something was not explained properly and/or is confusing you.

[DeshDeepakKant]

@m-umairx

1. I’ll have analyzed the existing cluster representation in clusters.py and also review all other code in the xrefer directory.

2. I think JSON could be the most suitable data structure for it

{
  "metadata": {
    "project": "XRefer Cluster Analysis",
    "version": "1.1",
    "generated_at": "2025-03-15T12:34:56Z",
    "description": "This file stores the function clusters extracted from a binary."
  },
  "clusters": [
    {
      "id": "C1",
      "root_node": "N1",
      "nodes": [
        {
          "id": "N1",
          "label": "main()",
          "description": "This is the entry point of the binary.",
          "properties": {
            "type": "function",
            "address": "0x400080",
            "calls": ["N2", "N3"]
          }
        },
        {
          "id": "N2",
          "label": "parse_input()",
          "description": "Responsible for parsing user input.",
          "properties": {
            "type": "function",
            "address": "0x4000A0",
            "calls": ["N4"]
          }
        }
      ],
      "edges": [
        { "source": "N1", "target": "N2", "relationship": "calls" },
        { "source": "N2", "target": "N4", "relationship": "calls" }
      ],
      "subclusters": [
        {
          "id": "C1.1",
          "root_node": "N3",
          "nodes": [
            {
              "id": "N3",
              "label": "sanitize_input()",
              "description": "Sanitizes user input before processing.",
              "properties": {
                "type": "function",
                "address": "0x4000C0",
                "calls": []
              }
            }
          ],
          "edges": [],
          "cluster_refs": {}
        }
      ],
      "cluster_refs": { "N4": "C2" },
      "intermediate_paths": { "N1-N4": ["N2"] }
    },
    {
      "id": "C2",
      "root_node": "N4",
      "nodes": [
        {
          "id": "N4",
          "label": "execute_command()",
          "description": "Executes the user-provided command.",
          "properties": {
            "type": "function",
            "address": "0x400100",
            "calls": ["N5"]
          }
        }
      ],
      "edges": [
        { "source": "N4", "target": "N5", "relationship": "calls" }
      ],
      "subclusters": [],
      "cluster_refs": {},
      "intermediate_paths": {}
    }
  ]
}

3. XRefer Data Extraction Points

Location in the Codebase

The primary files responsible for the extraction of data within XRefer are:

1. Analyzer File: [analyzer.py](https://github.com/mandiant/xrefer/blob/main/plugins/xrefer/core/analyzer.py)

   • Converts binary code into Python code.
   • Extracts key elements such as API calls, cross-references, libraries, and strings.
   • Clusters related functions to understand the program’s structure.

2. Clustering File: [clusters.py](https://github.com/mandiant/xrefer/blob/main/plugins/xrefer/core/clusters.py)

   • Creates a graph representation of function clusters.

Key Components for Data Extraction

Function Artifacts

• self.global_xrefs: Stores cross-references organized by function address.
• self.entities: Contains APIs, strings, libraries, and CAPA matches.
• self.table_data: Contextual data for functions.

Methods for Extracting Data

1. Function Analysis:

   • get_apis_for_function(func_ea): Extracts API calls.
   • get_libs_for_function(func_ea): Identifies libraries.
   • get_strings_for_function(func_ea): Extracts string references.
   • get_capa_for_function(func_ea): Extracts CAPA-based functionality.

2. API Call Information Extraction:

   • get_direct_calls(api_name, func_ea): Extracts direct API calls.
   • get_indirect_calls(api_name, func_ea): Extracts indirect API calls.
   • gather_sorted_function_api_calls(func_ea): Lists API calls in a function.

3. Cross-Reference Data Extraction:

   • generate_entity_xrefs_listing(e_index): Retrieves all cross-references for a given entity.

4. Cluster Information Extraction:

   • find_cluster_by_id(cluster_id): Retrieves cluster details.
   • self.cluster_analysis: Provides semantic analysis of the clusters.

[DeshDeepakKant]

Hi @m-umairx any thoughts on the proposed JSON format? Let me know if any modifications are needed and what the next steps should be. Thanks!