mukul975
cb8d79e068
- Add validated mitre_attack frontmatter to all 754 skills (286 distinct techniques), verified against MITRE ATT&CK v19.1 via the official mitreattack-python library: 0 revoked, deprecated, or invalid IDs - Curate precise per-skill technique IDs for forensics, malware-analysis, threat-intel, and red-team skills (e.g. DCSync -> T1003.006, Kerberoasting -> T1558.003, Pass-the-Ticket -> T1550.003) - Reconcile v19.1 tactic restructuring: Defense Evasion split into Stealth (TA0005) and Defense Impairment (TA0112); revoked T1562.* family and T1070.001/.002 remapped to active equivalents (T1685.*) - Normalize word-split tags across 35 skills (remove filename-derived stopword tags, add semantic cybersecurity tags) - Add api-reference.md for 3 skills that were missing it - Update README ATT&CK section with accurate v19.1 tactic distribution
2.3 KiB
name, description, domain, subdomain, tags, version, author, license, nist_csf, mitre_attack
| name | description | domain | subdomain | tags | version | author | license | nist_csf | mitre_attack | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| analyzing-heap-spray-exploitation | 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. | cybersecurity | malware-analysis |
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1.0 | mahipal | Apache-2.0 |
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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.