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
Unsupervised text tokenizer focused on computational efficiency

YouTokenToMe YouTokenToMe is an unsupervised text tokenizer focused on computational efficiency. It currently implements fast Byte Pair Encoding (BPE)

VK.com 847 Dec 19, 2022
AEC_DeepModel - Deep learning based acoustic echo cancellation baseline code

AEC_DeepModel - Deep learning based acoustic echo cancellation baseline code

凌逆战 75 Dec 05, 2022
Japanese NLP Library

Japanese NLP Library Back to Home Contents 1 Requirements 1.1 Links 1.2 Install 1.3 History 2 Libraries and Modules 2.1 Tokenize jTokenize.py 2.2 Cabo

Pulkit Kathuria 144 Dec 27, 2022
GAP-text2SQL: Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training

GAP-text2SQL: Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training Code and model from our AAAI 2021 paper

Amazon Web Services - Labs 83 Jan 09, 2023
A natural language modeling framework based on PyTorch

Overview PyText is a deep-learning based NLP modeling framework built on PyTorch. PyText addresses the often-conflicting requirements of enabling rapi

Meta Research 6.4k Jan 08, 2023
edge-SR: Super-Resolution For The Masses

edge-SR: Super Resolution For The Masses Citation Pablo Navarrete Michelini, Yunhua Lu and Xingqun Jiang. "edge-SR: Super-Resolution For The Masses",

Pablo 40 Nov 10, 2022
Implementation of COCO-LM, Correcting and Contrasting Text Sequences for Language Model Pretraining, in Pytorch

COCO LM Pretraining (wip) Implementation of COCO-LM, Correcting and Contrasting Text Sequences for Language Model Pretraining, in Pytorch. They were a

Phil Wang 44 Jul 28, 2022
Which Apple Keeps Which Doctor Away? Colorful Word Representations with Visual Oracles

Which Apple Keeps Which Doctor Away? Colorful Word Representations with Visual Oracles (TASLP 2022)

Zhuosheng Zhang 3 Apr 14, 2022
💛 Code and Dataset for our EMNLP 2021 paper: "Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes"

Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes Official PyTorch implementation and EmoCause evaluatio

Hyunwoo Kim 50 Dec 21, 2022
ChessCoach is a neural network-based chess engine capable of natural-language commentary.

ChessCoach is a neural network-based chess engine capable of natural-language commentary.

Chris Butner 380 Dec 03, 2022
One Stop Anomaly Shop: Anomaly detection using two-phase approach: (a) pre-labeling using statistics, Natural Language Processing and static rules; (b) anomaly scoring using supervised and unsupervised machine learning.

One Stop Anomaly Shop (OSAS) Quick start guide Step 1: Get/build the docker image Option 1: Use precompiled image (might not reflect latest changes):

Adobe, Inc. 148 Dec 26, 2022
Learning to Rewrite for Non-Autoregressive Neural Machine Translation

RewriteNAT This repo provides the code for reproducing our proposed RewriteNAT in EMNLP 2021 paper entitled "Learning to Rewrite for Non-Autoregressiv

Xinwei Geng 20 Dec 25, 2022
Simple translation demo showcasing our headliner package.

Headliner Demo This is a demo showcasing our Headliner package. In particular, we trained a simple seq2seq model on an English-German dataset. We didn

Axel Springer News Media & Tech GmbH & Co. KG - Ideas Engineering 16 Nov 24, 2022
Creating a Feed of MISP Events from ThreatFox (by abuse.ch)

ThreatFox2Misp Creating a Feed of MISP Events from ThreatFox (by abuse.ch) What will it do? This will fetch IOCs from ThreatFox by Abuse.ch, convert t

17 Nov 22, 2022
Simplified diarization pipeline using some pretrained models - audio file to diarized segments in a few lines of code

simple_diarizer Simplified diarization pipeline using some pretrained models. Made to be a simple as possible to go from an input audio file to diariz

Chau 65 Dec 30, 2022
NLP, Machine learning

Netflix-recommendation-system NLP, Machine learning About Recommendation algorithms are at the core of the Netflix product. It provides their members

Harshith VH 6 Jan 12, 2022
Understanding the Difficulty of Training Transformers

Admin Understanding the Difficulty of Training Transformers Guided by our analyses, we propose Adaptive Model Initialization (Admin), which successful

Liyuan Liu 300 Dec 29, 2022
CoSENT、STS、SentenceBERT

CoSENT_Pytorch 比Sentence-BERT更有效的句向量方案

102 Dec 07, 2022
自然言語で書かれた時間情報表現を抽出/規格化するルールベースの解析器

ja-timex 自然言語で書かれた時間情報表現を抽出/規格化するルールベースの解析器 概要 ja-timex は、現代日本語で書かれた自然文に含まれる時間情報表現を抽出しTIMEX3と呼ばれるアノテーション仕様に変換することで、プログラムが利用できるような形に規格化するルールベースの解析器です。

Yuki Okuda 116 Nov 09, 2022
A method to generate speech across multiple speakers

VoiceLoop PyTorch implementation of the method described in the paper VoiceLoop: Voice Fitting and Synthesis via a Phonological Loop. VoiceLoop is a n

Facebook Archive 873 Dec 15, 2022