abusing-dpapi-for-cred…
Extract and decrypt Windows DPAPI-protected secrets (Credential Manager, browser logins/cookies, Wi-Fi credentials, KeePass keys) online or offline using…
Detect and analyze heap spray attacks in memory dumps using Volatility3
$ npx -y skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-heap-spray-exploitation --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/analyzing-heap-spray-exploitationContext preview
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Detect and analyze heap spray attacks in memory dumps using Volatility3
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
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.
Use Volatility3 windows.malfind to scan for processes with executable injected memory regions.
Examine VAD tree entries using windows.vadinfo for large contiguous allocations with RWX permissions.
Search suspicious memory regions for NOP sled signatures (0x90 sequences, 0x0c0c0c0c patterns).
Dump suspicious memory regions and identify shellcode using byte pattern analysis.
JSON report with suspicious processes, heap spray indicators, NOP sled locations, memory region sizes, and extracted shellcode hashes.
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 · Apache 2.0
Repo: mukul975/Anthropic-Cybersecurity-Skills
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