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分析堆噴射利用

使用 Volatility3 檢測並分析記憶體轉儲中的堆噴射攻擊。

分析堆噴射利用 是什麼?

該技能專注於利用 Volatility3 框架檢測和分析記憶體轉儲中的堆噴射利用攻擊。堆噴射是一種常見的漏洞利用技術,攻擊者會在進程堆中填入大量受控數據(例如 NOP sled 和 shellcode),以提高代碼執行的可靠性。此技能涵蓋使用 Volatility3 的 malfind、vadinfo 和 memmap 插件查找可疑的連續記憶體分配,掃描 NOP sled 模式(如 0x90 和 0x0c0c0c0c),並提取內嵌的 shellcode 進行進一步分析的完整流程。它適用於安全事件調查、檢測規則構建、威脅獵殺查詢以及 SOC 分析師的結構化分析。支援的記憶體轉儲格式包括 .raw、.vmem 和 .dmp,最終輸出為 JSON 報告,其中包含可疑進程、堆噴射指標、NOP sled 位置、記憶體區域大小以及提取的 shellcode 的哈希值。

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說明文件

分析堆噴射利用技術

概述

堆噴射是一種利用技術,透過將攻擊者控制的資料(通常是 NOP 滑行區後接 shellcode)填入程式堆疊的大片區域,以提升程式碼執行漏洞的可靠度。本技能涵蓋使用 Volatility3 的 malfind、vadinfo 及 memmap 外掛在記憶體傾印檔中偵測堆噴射痕跡、識別可疑的連續記憶體配置、掃描 NOP 滑行模式(0x90、0x0c0c0c0c),以及萃取內嵌的 shellcode 進行分析。

使用時機

  • 調查涉及分析堆噴射利用技術的安全性事件時
  • 建立此領域的偵測規則或威脅獵捕查詢時
  • SOC 分析師需要此類分析的結構化程序時
  • 驗證相關攻擊技術的安全監控覆蓋範圍時

先決條件

  • 已安裝 volatility3 框架的 Python 3.9+ 環境
  • 記憶體傾印檔(.raw、.vmem、.dmp 格式)
  • 了解虛擬記憶體佈局與 VAD(虛擬位址描述元)樹
  • 熟悉常見的 shellcode 模式與 NOP 滑行編碼

步驟

步驟 1:識別可疑程序

使用 Volatility3 windows.malfind 掃描具有可執行注入記憶體區域的程序。

步驟 2:分析 VAD 項目

使用 windows.vadinfo 檢查 VAD 樹項目,尋找具有 RWX 權限的大型連續配置。

步驟 3:掃描 NOP 滑行模式

搜尋可疑記憶體區域中的 NOP 滑行特徵(0x90 序列、0x0c0c0c0c 模式)。

步驟 4:萃取並分析 Shellcode

傾印可疑記憶體區域,並透過位元組模式分析識別 shellcode。

預期輸出

JSON 報告,內含可疑程序、堆噴射指標、NOP 滑行位置、記憶體區域大小以及萃取出的 shellcode 雜湊值。

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

abusing-dpapi-for-credential-access

Extract and decrypt Windows DPAPI-protected secrets (Credential Manager, browser logins/cookies, Wi-Fi credentials, KeePass keys) online or offline using SharpDPAPI, SharpChrome, Mimikatz, or Impacket

abusing-shadow-credentials-for-privesc

Take over Active Directory accounts by writing attacker-controlled public keys to msDS-KeyCredentialLink (Shadow Credentials) with pyWhisker, Whisker, or Certipy, then authenticate via PKINIT to recover the target

achieving-cmmc-level-2-compliance

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acquiring-disk-image-with-dd-and-dcfldd

Create forensically sound bit-for-bit disk images with dd or dcfldd on a Linux forensic workstation, preserving evidence integrity through hash verification (MD5/SHA) during acquisition. Use when imaging a suspect drive, USB device, or memory card for investigation, preserving volatile disk evidence during incident response, or producing a verified copy for legal or law-enforcement proceedings before any destructive analysis.

analyzing-active-directory-acl-abuse

Detect dangerous ACL misconfigurations in Active Directory using ldap3

analyzing-android-malware-with-apktool

Perform static analysis of Android APK malware using apktool for resource decompilation, jadx for Java source recovery, and androguard for manifest inspection, dangerous permission-combination detection, and identification of obfuscated code, dynamic code loading, and reflection-based API calls. Use to statically triage a suspicious APK without executing it or to build mobile malware detection rules.

analyzing-api-gateway-access-logs

Parses API Gateway access logs (AWS API Gateway, Kong, Nginx) to detect

analyzing-apt-group-with-mitre-navigator

Query ATT&CK data with attackcti, mitreattack-python, and stix2, then build MITRE ATT&CK Navigator layers and multi-layer heatmap overlays mapping one or more APT groups

analyzing-azure-activity-logs-for-threats

Queries Azure Monitor activity logs and sign-in logs via azure-monitor-query

analyzing-bootkit-and-rootkit-samples

Analyzes bootkit and advanced rootkit malware infecting the Master

analyzing-browser-forensics-with-hindsight

Parse Chromium-based browser databases with Hindsight to extract and correlate browsing history, downloads, cookies, cached content, autofill data, saved passwords, and extensions from Chrome, Edge, Brave, Opera, and Vivaldi into a unified timeline (XLSX, JSON, or SQLite output). Use during incident response, insider-threat investigations, or criminal cases when you need to reconstruct a user

analyzing-campaign-attribution-evidence

Systematically evaluate cyber-campaign evidence to attribute an operation to a threat actor, using the Diamond Model and Analysis of Competing Hypotheses (ACH) to weigh infrastructure overlaps, TTP consistency, malware code similarity, and timing/language artifacts into confidence-weighted attribution assessments. Use when an incident investigation needs a defensible attribution confidence level.

analyzing-certificate-transparency-for-phishing

Monitor Certificate Transparency logs using crt.sh and Certstream to

analyzing-cloud-storage-access-patterns

Detect abnormal access in AWS S3, GCS, and Azure Blob Storage by analyzing CloudTrail Data Events, GCS audit logs, and Azure Storage Analytics for after-hours bulk downloads, new-IP access, and API-call spikes (e.g. GetObject) via statistical baselines and time-series anomaly detection. Use when investigating suspected cloud data exfiltration or building related detection rules.

analyzing-cobalt-strike-beacon-configuration

Extract and analyze Cobalt Strike beacon configuration from PE files

analyzing-cobaltstrike-malleable-c2-profiles

Parse and analyze Cobalt Strike Malleable C2 profiles with dissect.cobaltstrike (profiles and beacon-payload configs) and pyMalleableC2 (AST parsing) to extract HTTP/DNS transforms, URIs, headers, sleep/jitter, and injection behavior, then generate network detection signatures. Use when reverse-engineering a captured malleable profile or building detections against Cobalt Strike Beacon traffic.

analyzing-command-and-control-communication

Analyzes malware C2 communication over HTTP, HTTPS, DNS, and custom

analyzing-cyber-kill-chain

Analyzes intrusion activity against the Lockheed Martin Cyber Kill Chain

analyzing-disk-image-with-autopsy

Perform comprehensive forensic analysis of raw (dd), E01, or AFF disk images with Autopsy and The Sleuth Kit, recovering deleted files, examining metadata and embedded artifacts, keyword searching, and building investigation timelines with visual reports. Use for structured analysis of a forensic disk image or when stakeholders need visual reports from evidence.

analyzing-dns-logs-for-exfiltration

Analyzes DNS query logs to detect data exfiltration via DNS tunneling,

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