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堆喷利用分析

检测和分析内存转储中的堆喷攻击,使用 Volatility3 框架。

堆喷利用分析 是什么?

该技能专注于利用 Volatility3 框架检测和分析内存转储中的堆喷攻击。堆喷是一种利用技术,攻击者通过在大片堆内存中填充受控数据(通常为 NOP 滑板后接 shellcode)来提高代码执行漏洞的可靠性。技能通过 malfind、vadinfo 和 memmap 插件识别可疑进程和连续内存分配,扫描 NOP 滑板模式(0x90、0x0c0c0c0c),并提取嵌入的 shellcode 进行分析。适用于安全事件调查、检测规则构建、威胁狩猎查询以及 SOC 分析师的结构化分析流程。支持 Windows 内存转储格式,输出包含可疑进程、堆喷指示器、NOP 滑板位置、内存区域大小及 shellcode 哈希的 JSON 报告。

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文档

堆喷利用分析 是做什么的?

概述

堆喷是一种利用技术,攻击者通过填充进程堆中的大片区域(通常为 NOP 滑板后接 shellcode)来提高代码执行漏洞的可靠性。本技能涵盖使用 Volatility3 的 malfind、vadinfo 和 memmap 插件检测内存转储中的堆喷痕迹,识别可疑的连续内存分配,扫描 NOP 滑板模式(0x90、0x0c0c0c0c),并提取嵌入的 shellcode 进行分析。

使用场景

  • 调查需要分析堆喷利用的安全事件时
  • 构建此领域的检测规则或威胁狩猎查询时
  • SOC 分析师需要该分析类型的结构化程序时
  • 验证相关攻击技术的安全监控覆盖范围时

前提条件

  • Python 3.9+ 并安装 volatility3 框架
  • 内存转储文件(.raw、.vmem、.dmp 格式)
  • 理解虚拟内存布局和 VAD(虚拟地址描述符)树
  • 熟悉常见的 shellcode 模式及 NOP 滑板编码

步骤

步骤 1:识别可疑进程

使用 Volatility3 windows.malfind 扫描含有可执行注入内存区域的进程。

步骤 2:分析 VAD 条目

使用 windows.vadinfo 检查 VAD 树条目,查找具有 RWX 权限的大型连续内存分配。

步骤 3:扫描 NOP 滑板模式

在可疑内存区域中搜索 NOP 滑板特征(0x90 序列、0x0c0c0c0c 模式)。

步骤 4:提取并分析 Shellcode

转储可疑内存区域,并通过字节模式分析识别 shellcode。

预期输出

包含可疑进程、堆喷指示器、NOP 滑板位置、内存区域大小以及提取的 shellcode 哈希的 JSON 报告。

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

>-

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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