Malware Analysis

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Forensics CheatSheets

https://www.jaiminton.com/cheatsheet/DFIR/#

Online Services

• VirusTotal
• HybridAnalysis
• Koodous
• Intezer
• Any.Run

Offline Antivirus and Detection Tools

Yara

Install

sudo apt-get install -y yara

Prepare rules

Use this script to download and merge all the yara malware rules from github: https://gist.github.com/andreafortuna/29c6ea48adf3d45a979a78763cdc7ce9
Create the rules directory and execute it. This will create a file called malware_rules.yar which contains all the yara rules for malware.

wget https://gist.githubusercontent.com/andreafortuna/29c6ea48adf3d45a979a78763cdc7ce9/raw/4ec711d37f1b428b63bed1f786b26a0654aa2f31/malware_yara_rules.py
mkdir rules
python malware_yara_rules.py

Scan

yara -w malware_rules.yar image  #Scan 1 file
yara -w malware_rules.yar folder #Scan the whole folder

YaraGen: Check for malware and Create rules

You can use the tool YaraGen to generate yara rules from a binary. Check out these tutorials: Part 1, Part 2, Part 3

 python3 yarGen.py --update
 python3.exe yarGen.py --excludegood -m  ../../mals/

ClamAV

Install

sudo apt-get install -y clamav

Scan

sudo freshclam      #Update rules
clamscan filepath   #Scan 1 file
clamscan folderpath #Scan the whole folder

Capa

Capa detects potentially malicious capabilities in executables: PE, ELF, .NET. So it will find things such as Att&ck tactics, or suspicious capabilities such as:

• check for OutputDebugString error
• run as a service
• create process

Get it int he Github repo.

IOCs

IOC means Indicator Of Compromise. An IOC is a set of conditions that identify some potentially unwanted software or confirmed malware. Blue Teams use this kind of definition to search for this kind of malicious files in their systems and networks.
To share these definitions is very useful as when malware is identified in a computer and an IOC for that malware is created, other Blue Teams can use it to identify the malware faster.

A tool to create or modify IOCs is IOC Editor.
You can use tools such as Redline to search for defined IOCs in a device.

Loki

Loki is a scanner for Simple Indicators of Compromise.
Detection is based on four detection methods:

1. File Name IOC
   Regex match on full file path/name

2. Yara Rule Check
   Yara signature matches on file data and process memory

3. Hash Check
   Compares known malicious hashes (MD5, SHA1, SHA256) with scanned files

4. C2 Back Connect Check
   Compares process connection endpoints with C2 IOCs (new since version v.10)

Linux Malware Detect

Linux Malware Detect (LMD) is a malware scanner for Linux released under the GNU GPLv2 license, that is designed around the threats faced in shared hosted environments. It uses threat data from network edge intrusion detection systems to extract malware that is actively being used in attacks and generates signatures for detection. In addition, threat data is also derived from user submissions with the LMD checkout feature and malware community resources.

rkhunter

Tools like rkhunter can be used to check the filesystem for possible rootkits and malware.

sudo ./rkhunter --check -r / -l /tmp/rkhunter.log [--report-warnings-only] [--skip-keypress]

FLOSS

FLOSS is a tool that will try to find obfuscated strings inside executables using different techniques.

PEpper

PEpper checks some basic stuff inside the executable (binary data, entropy, URLs and IPs, some yara rules).

PEstudio

PEstudio is a tool that allows to get information of Windows executables such as imports, exports, headers, but also will check virus total and find potential Att&ck techniques.

Detect It Easy(DiE)

DiE is a tool to detect if a file is encrypted and also find packers.

NeoPI

NeoPI is a Python script that uses a variety of statistical methods to detect obfuscated and encrypted content within text/script files. The intended purpose of NeoPI is to aid in the detection of hidden web shell code.

php-malware-finder

PHP-malware-finder does its very best to detect obfuscated/dodgy code as well as files using PHP functions often used in malwares/webshells.

Apple Binary Signatures

When checking some malware sample you should always check the signature of the binary as the developer that signed it may be already related with malware.

#Get signer
codesign -vv -d /bin/ls 2>&1 | grep -E "Authority|TeamIdentifier"

#Check if the app’s contents have been modified
codesign --verify --verbose /Applications/Safari.app

#Check if the signature is valid
spctl --assess --verbose /Applications/Safari.app

Detection Techniques

File Stacking

If you know that some folder containing the files of a web server was last updated on some date. Check the date all the files in the web server were created and modified and if any date is suspicious, check that file.

Baselines

If the files of a folder shouldn't have been modified, you can calculate the hash of the original files of the folder and compare them with the current ones. Anything modified will be suspicious.

Statistical Analysis

When the information is saved in logs you can check statistics like how many times each file of a web server was accessed as a web shell might be one of the most.

Deobfuscating Dynamic Control-Flow (JMP/CALL RAX Dispatchers)

Modern malware families heavily abuse Control-Flow Graph (CFG) obfuscation: instead of a direct jump/call they compute the destination at run-time and execute a jmp rax or call rax. A small dispatcher (typically nine instructions) sets the final target depending on the CPU ZF/CF flags, completely breaking static CFG recovery.

The technique – showcased by the SLOW#TEMPEST loader – can be defeated with a three-step workflow that only relies on IDAPython and the Unicorn CPU emulator.

1. Locate every indirect jump / call

import idautils, idc

for ea in idautils.FunctionItems(idc.here()):
    mnem = idc.print_insn_mnem(ea)
    if mnem in ("jmp", "call") and idc.print_operand(ea, 0) == "rax":
        print(f"[+] Dispatcher found @ {ea:X}")

2. Extract the dispatcher byte-code

import idc

def get_dispatcher_start(jmp_ea, count=9):
    s = jmp_ea
    for _ in range(count):
        s = idc.prev_head(s, 0)
    return s

start = get_dispatcher_start(jmp_ea)
size  = jmp_ea + idc.get_item_size(jmp_ea) - start
code  = idc.get_bytes(start, size)
open(f"{start:X}.bin", "wb").write(code)

3. Emulate it twice with Unicorn

from unicorn import *
from unicorn.x86_const import *
import struct

def run(code, zf=0, cf=0):
    BASE = 0x1000
    mu = Uc(UC_ARCH_X86, UC_MODE_64)
    mu.mem_map(BASE, 0x1000)
    mu.mem_write(BASE, code)
    mu.reg_write(UC_X86_REG_RFLAGS, (zf << 6) | cf)
    mu.reg_write(UC_X86_REG_RAX, 0)
    mu.emu_start(BASE, BASE+len(code))
    return mu.reg_read(UC_X86_REG_RAX)

Run run(code,0,0) and run(code,1,1) to obtain the false and true branch targets.

4. Patch back a direct jump / call

import struct, ida_bytes

def patch_direct(ea, target, is_call=False):
    op   = 0xE8 if is_call else 0xE9           # CALL rel32 or JMP rel32
    disp = target - (ea + 5) & 0xFFFFFFFF
    ida_bytes.patch_bytes(ea, bytes([op]) + struct.pack('<I', disp))

After patching, force IDA to re-analyse the function so the full CFG and Hex-Rays output are restored:

import ida_auto, idaapi
idaapi.reanalyze_function(idc.get_func_attr(ea, idc.FUNCATTR_START))

5. Label indirect API calls

Once the real destination of every call rax is known you can tell IDA what it is so parameter types & variable names are recovered automatically:

idc.set_callee_name(call_ea, resolved_addr, 0)  # IDA 8.3+

Practical benefits

• Restores the real CFG → decompilation goes from 10 lines to thousands.
• Enables string-cross-reference & xrefs, making behaviour reconstruction trivial.
• Scripts are reusable: drop them into any loader protected by the same trick.

References

• Unit42 – Evolving Tactics of SLOW#TEMPEST: A Deep Dive Into Advanced Malware Techniques