Various capabilities for static malware analysis.

Overview

Malchive

The malchive serves as a compendium for a variety of capabilities mainly pertaining to malware analysis, such as scripts supporting day to day binary analysis and decoder modules for various components of malicious code.

The goals behind the 'malchive' are to:

  • Allow teams to centralize efforts made in this realm and enforce communication and continuity
  • Have a shared corpus of tools for people to build on
  • Enforce clean coding practices
  • Allow others to interface with project members to develop their own capabilities
  • Promote a positive feedback loop between Threat Intel and Reverse Engineering staff
  • Make static file analysis more accessible
  • Serve as a vehicle to communicate the unique opportunity space identified via deep dive analysis

Documentation

At its core, malchive is a bunch of standalone scripts organized in a manner that the authors hope promotes the project's goals.

To view the documentation associated with this project, checkout the wiki page!

Scripts within the malchive are split up into the following core categories:

  • Utilities - These scripts may be run standalone to assist with static binary analysis or as modules supporting a broader program. Utilities always have a standalone component.
  • Helpers - These modules primarily serve to assist components in one or more of the other categories. They generally do not have a stand-alone component and instead serve the intents of those that do.
  • Binary Decoders - The purpose of scripts in this category is to retrieve, decrypt, and return embedded data (typically inside malware).
  • Active Discovery - Standalone scripts designed to emulate a small portion of a malware family's protocol for the purposes of discovering active controllers.

Installation

The malchive is a packaged distribution that is easily installed and will automatically create console stand-alone scripts.

Steps

You will need to install some dependencies for some of the required Python modules to function correctly.

  • First do a source install of YARA and make sure you compile using --dotnet
  • Next source install the YARA Python package.
  • Ensure you have sqlite3-dev installed
    • Debian: libsqlite3-dev
    • Red Hat: sqlite-devel / pip install pysqlite3

You can then clone the malchive repo and install...

  • pip install . when in the parent directory.
  • To remove, just pip uninstall malchive

Scripts

Console scripts stemming from utilities are appended with the prefix malutil, decoders are appended with maldec, and active discovery scripts are appended with maldisc. This allows for easily identifiable malchive scripts via tab autocompletion.

; running superstrings from cmd line
malutil-superstrings 1.exe -ss
0x9535 (stack) lstrlenA
0x9592 (stack) GetFileSize
0x95dd (stack) WriteFile
0x963e (stack) CreateFileA
0x96b0 (stack) SetFilePointer
0x9707 (stack) GetSystemDirectoryA

; running a decoder from cmd line
maldec-pivy test.exe_
{
    "MD5": "2973ee05b13a575c06d23891ab83e067",
    "Config": {
        "PersistActiveSetupName": "StubPath",
        "DefaultBrowserKey": "SOFTWARE\\Classes\\http\\shell\\open\\command",
        "PersistActiveSetupKeyPart": "Software\\Microsoft\\Active Setup\\Installed Components\\",
        "ServerId": "TEST - WIN_XP",
        "Callbacks": [
            {
                "callback": "192.168.1.104",
                "protocol": "Direct",
                "port": 3333
            },
            {
                "callback": "192.168.1.111",
                "protocol": "Direct",
                "port": 4444
            }
        ],
        "ProxyCfgPresent": false,
        "Password": "test$321$",
        "Mutex": ")#V0qA.I4",
        "CopyAsADS": true,
        "Melt": true,
        "InjectPersist": true,
        "Inject": true
    }
}

; cmd line use with other common utilities
echo -ne 'eJw9kLFuwzAMRIEC7ZylrVGgRSFZiUbBZmwqsMUP0VfcnuQn+rMde7KLTBIPj0ce34tHyMUJjrnw
p3apz1kicjoJrDRlQihwOXmpL4RmSR5qhEU9MqvgWo8XqGMLJd+sKNQPK0dIGjK+e5WANIT6NeOs
k2mI5NmYAmcrkbn4oLPK5gZX+hVlRoKloMV20uQknv2EPunHKQtcig1cpHY4Jodie5pRViV+rp1t
629J6Dyu4hwLR97LINqY5rYILm1hhlvinoyJZavOKTrwBHTwpZ9yPSzidUiPt8PUTkZ0FBfayWLp
a71e8U8YDrbtu0aWDj+/eBOu+jRkYabX+3hPu9LZ5fb41T+7fmRf' | base64 -d | zlib-flate -uncompress | malutil-xor - [KEY]

Interfacing

Utilities, decoders, and discovery scripts in this collection are designed to support single ad-hoc analysis as well as inclusion into other frameworks. After installation, the malchive should be part of your Python path. At this point accessing any of the scripts is straight forward.

Here are a few examples:

; accessing decoder modules
import sys
from malchive.decoders import testdecoder

p = testdecoder.GetConfig(open(sys.argv[1], 'rb').read())
print('password', p.rc4_key)
for c in p.callbacks:
    print('c2 address', c)

; accessing utilities
from malchive.utilities import xor
ret = xor.GenericXor(buff=b'testing', key=[0x51], count=0xff)
print(ret.run_crypt())

; accessing helpers
from malchive.helpers import winfunc
key = winfunc.CryptDeriveKey(b'testdatatestdata')

To understand more about a given module, see the associated wiki entry.

Contributing

Contributing to the malchive is easy, just ensure the following requirements are met:

  • When writing utilities, decoders, or discovery scripts, consider using the available templates or review existing code if you're not sure how to get started.
  • Make sure modification or contributions pass pre-commit tests.
  • Ensure the contribution is placed in one of the component folders.
  • Updated the setup file if needed with an entry.
  • Python3 is a must.

Legal

©2021 The MITRE Corporation. ALL RIGHTS RESERVED.

Approved for Public Release; Distribution Unlimited. Public Release Case Number 21-0153

Owner
MITRE Cybersecurity
MITRE Cybersecurity
NLP project that works with news (NER, context generation, news trend analytics)

СоАвтор СоАвтор – платформа и открытый набор инструментов для редакций и журналистов-фрилансеров, который призван сделать процесс создания контента ма

38 Jan 04, 2023
Large-scale Knowledge Graph Construction with Prompting

Large-scale Knowledge Graph Construction with Prompting across tasks (predictive and generative), and modalities (language, image, vision + language, etc.)

ZJUNLP 161 Dec 28, 2022
Use the state-of-the-art m2m100 to translate large data on CPU/GPU/TPU. Super Easy!

Easy-Translate is a script for translating large text files in your machine using the M2M100 models from Facebook/Meta AI. We also privide a script fo

Iker García-Ferrero 41 Dec 15, 2022
FastFormers - highly efficient transformer models for NLU

FastFormers FastFormers provides a set of recipes and methods to achieve highly efficient inference of Transformer models for Natural Language Underst

Microsoft 678 Jan 05, 2023
Abhijith Neil Abraham 2 Nov 05, 2021
Sequence-to-sequence framework with a focus on Neural Machine Translation based on Apache MXNet

Sequence-to-sequence framework with a focus on Neural Machine Translation based on Apache MXNet

Amazon Web Services - Labs 1.1k Dec 27, 2022
Python library for interactive topic model visualization. Port of the R LDAvis package.

pyLDAvis Python library for interactive topic model visualization. This is a port of the fabulous R package by Carson Sievert and Kenny Shirley. pyLDA

Ben Mabey 1.7k Dec 20, 2022
This repository details the steps in creating a Part of Speech tagger using Trigram Hidden Markov Models and the Viterbi Algorithm without using external libraries.

POS-Tagger This repository details the creation of a Part-of-Speech tagger using Trigram Hidden Markov Models to predict word tags in a word sequence.

Raihan Ahmed 1 Dec 09, 2021
A multi-voice TTS system trained with an emphasis on quality

TorToiSe Tortoise is a text-to-speech program built with the following priorities: Strong multi-voice capabilities. Highly realistic prosody and inton

James Betker 2.1k Jan 01, 2023
NLP - Machine learning

Flipkart-product-reviews NLP - Machine learning About Product reviews is an essential part of an online store like Flipkart’s branding and marketing.

Harshith VH 1 Oct 29, 2021
A fast and easy implementation of Transformer with PyTorch.

FasySeq FasySeq is a shorthand as a Fast and easy sequential modeling toolkit. It aims to provide a seq2seq model to researchers and developers, which

宁羽 7 Jul 18, 2022
Takes a string and puts it through different languages in Google Translate a requested amount of times, returning nonsense.

PythonTextObfuscator Takes a string and puts it through different languages in Google Translate a requested amount of times, returning nonsense. Requi

2 Aug 29, 2022
Reading Wikipedia to Answer Open-Domain Questions

DrQA This is a PyTorch implementation of the DrQA system described in the ACL 2017 paper Reading Wikipedia to Answer Open-Domain Questions. Quick Link

Facebook Research 4.3k Jan 01, 2023
nlp基础任务

NLP算法 说明 此算法仓库包括文本分类、序列标注、关系抽取、文本匹配、文本相似度匹配这五个主流NLP任务,涉及到22个相关的模型算法。 框架结构 文件结构 all_models ├── Base_line │   ├── __init__.py │   ├── base_data_process.

zuxinqi 23 Sep 22, 2022
基于Transformer的单模型、多尺度的VAE模型

UniVAE 基于Transformer的单模型、多尺度的VAE模型 介绍 https://kexue.fm/archives/8475 依赖 需要大于0.10.6版本的bert4keras(当前还没有推到pypi上,可以直接从GitHub上clone最新版)。 引用 @misc{univae,

苏剑林(Jianlin Su) 49 Aug 24, 2022
Final Project Bootcamp Zero

The Quest (Pygame) Descripción Este es el repositorio de código The-Quest para el proyecto final Bootcamp Zero de KeepCoding. El juego consiste en la

Seven-z01 1 Mar 02, 2022
Linking data between GBIF, Biodiverse, and Open Tree of Life

GBIF-biodiverse-OpenTree Linking data between GBIF, Biodiverse, and Open Tree of Life The python scripts will rely on opentree and Dendropy. To set up

2 Oct 03, 2022
UniSpeech - Large Scale Self-Supervised Learning for Speech

UniSpeech The family of UniSpeech: WavLM (arXiv): WavLM: Large-Scale Self-Supervised Pre-training for Full Stack Speech Processing UniSpeech (ICML 202

Microsoft 281 Dec 15, 2022
Guide: Finetune GPT2-XL (1.5 Billion Parameters) and GPT-NEO (2.7 B) on a single 16 GB VRAM V100 Google Cloud instance with Huggingface Transformers using DeepSpeed

Guide: Finetune GPT2-XL (1.5 Billion Parameters) and GPT-NEO (2.7 Billion Parameters) on a single 16 GB VRAM V100 Google Cloud instance with Huggingfa

289 Jan 06, 2023
A collection of Classical Chinese natural language processing models, including Classical Chinese related models and resources on the Internet.

GuwenModels: 古文自然语言处理模型合集, 收录互联网上的古文相关模型及资源. A collection of Classical Chinese natural language processing models, including Classical Chinese related models and resources on the Internet.

Ethan 66 Dec 26, 2022