analyzing-heap-spray-exploitation

Solid

Detect and analyze heap spray attacks in memory dumps using Volatility3 plugins to identify NOP sled patterns, shellcode landing zones, and suspicious large allocations in process virtual address space.

AI & Automation 40 stars 10 forks Updated today MIT

Install

View on GitHub

Quality Score: 88/100

Stars 20%
54
Recency 20%
100
Frontmatter 20%
70
Documentation 15%
92
Issue Health 10%
50
License 10%
100
Description 5%
100

Skill Content

# Analyzing Heap Spray Exploitation ## Overview Heap spraying is an exploitation technique that fills large regions of a process's heap with attacker-controlled data (typically NOP sleds followed by shellcode) to increase the reliability of code execution exploits. This skill covers detecting heap spray artifacts in memory dumps using Volatility3's malfind, vadinfo, and memmap plugins, identifying suspicious contiguous memory allocations, scanning for NOP sled patterns (0x90, 0x0c0c0c0c), and extracting embedded shellcode for analysis. ## When to Use - When investigating security incidents that require analyzing heap spray exploitation - When building detection rules or threat hunting queries for this domain - When SOC analysts need structured procedures for this analysis type - When validating security monitoring coverage for related attack techniques ## Prerequisites - Python 3.9+ with `volatility3` framework installed - Memory dump file (.raw, .vmem, .dmp format) - Understanding of virtual memory layout and VAD (Virtual Address Descriptor) trees - Familiarity with common shellcode patterns and NOP sled encodings ## Steps ### Step 1: Identify Suspicious Processes Use Volatility3 windows.malfind to scan for processes with executable injected memory regions. ### Step 2: Analyze VAD Entries Examine VAD tree entries using windows.vadinfo for large contiguous allocations with RWX permissions. ### Step 3: Scan for NOP Sled Patterns Search suspicious memory regions for ...

Details

Author
26zl
Repository
26zl/cybersec-toolkit
Created
6 months ago
Last Updated
today
Language
Python
License
MIT

Integrates with

Similar Skills

Semantically similar based on skill content — not just same category