What it does. Detect and analyze heap spray attacks in memory dumps using Volatility3 Part of mukul975/Anthropic-Cybersecurity-Skills (817 security skills) (mukul975/Anthropic-Cybersecurity-Skills).

Install

• npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-heap-spray-exploitation, or copy the skill folder into ~/.claude/skills/analyzing-heap-spray-exploitation/.
• Raw file: curl -sL https://raw.githubusercontent.com/mukul975/Anthropic-Cybersecurity-Skills/HEAD/skills/analyzing-heap-spray-exploitation/SKILL.md

SKILL.md (verbatim)

name: analyzing-heap-spray-exploitation
description: 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.
domain: cybersecurity
subdomain: malware-analysis
tags:
- malware-analysis
- memory-forensics
- heap-spray
- volatility3
- exploit-analysis
version: '1.0'
author: mahipal
license: Apache-2.0
nist_csf:
- DE.AE-02
- RS.AN-03
- ID.RA-01
- DE.CM-01
mitre_attack:
- T1203
- T1059.007
- T1106

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 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.

Other files in this skill

• LICENSE
• references/api-reference.md
• scripts/agent.py

references/api-reference.md (verbatim)

API Reference: Analyzing Heap Spray Exploitation

Volatility3 Plugins for Heap Spray Analysis

Common Heap Spray NOP Sled Patterns

Shellcode Signatures

Detection Thresholds

Install Volatility3

pip install volatility3
# Or from source:
git clone https://github.com/volatilityfoundation/volatility3.git
cd volatility3 && pip install -e .

References

• Volatility3 GitHub: https://github.com/volatilityfoundation/volatility3
• Volatility3 malfind: https://volatility3.readthedocs.io/en/latest/
• Heap Spray Techniques: https://www.corelan.be/index.php/2011/12/31/exploit-writing-tutorial-part-11-heap-spraying-demystified/
• DFRWS 2025 Workshop: https://webdiis.unizar.es/~ricardo/dfrws-eu-25-workshop/

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