analyzing-heap-spray-exploitation
Lets your agent find and analyze attacks that try to use computer memory maliciously. It looks for suspicious patterns in memory.
What it does
This tool helps find and analyze 'heap spray' attacks in memory files. It looks for patterns of malicious code that hackers use to run their programs.
When to use it
- ✓When investigating a security incident.
- ✓To create attack detection rules.
- ✓To check if security is working against these attacks.
Example of what to ask your AI
“Analyze this memory file for heap spray attacks and tell me what you find.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The tool uses a program called Volatility3 to examine memory. It looks for suspicious areas, known attack code patterns, and tries to extract the malicious code for analysis.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-heap-spray-exploitationOr download just the file:
curl -L https://raw.githubusercontent.com/mukul975/Anthropic-Cybersecurity-Skills/main/skills/analyzing-heap-spray-exploitation/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
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
volatility3framework 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 NOP sled signatures (0x90 sequences, 0x0c0c0c0c patterns).
Step 4: Extract and Analyze Shellcode
Dump suspicious memory regions and identify shellcode using byte pattern analysis.
Expected Output
JSON report with suspicious processes, heap spray indicators, NOP sled locations, memory region sizes, and extracted shellcode hashes.
FAQ
What is 'heap spray'?
'Heap spray' is a technique used by hackers to ensure their malicious code gets executed. They fill a large part of memory with data they control, increasing the chances of success.
What are 'NOP sleds'?
'NOP sleds' are sequences of instructions that do nothing (NOP means 'No Operation'). They are used before the malicious code to 'slide' execution to the main attack code.
What is Volatility3?
Volatility3 is a tool used to analyze a computer's memory. It helps understand what was happening on a system, especially during or after a security incident.
Details
Original description by the author
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.
Popularity
- GitHub stars
- 35k
- Ranking position
- #5452
- License
- Apache-2.0
- Updated
- 8/31/2026
Comments
- …
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