Analyzing Heap Spray ExploitationSAFE

817 structured cybersecurity skills for AI agents · Mapped to 6 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, NIST AI RMF & MITRE F3 (Fight Fraud) · agentskills.io standard · Works with Claude Code, GitHub Copilot, Codex CLI, Cursor, Gemini CLI & 20+ platforms · 29 security domains ·

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
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.

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

What the source does

Filesystem

none-observed

Network

none-observed

Shell

none-observed

Dependencies

pinned

Secrets in source

none-found

Findings (0)

No findings outside the package's declared scope.

Gates applied: no_behavioural_pass.