[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6AE3DDFEN33QYW0N0NTEYVM":3,"skill-same:01M6AE3DDFEN33QYW0N0NTEYVM":101},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":60,"commitSha":61,"skillDirectory":62,"forksCount":63,"repositoryHtmlUrl":64,"repositoryHomepageUrl":65,"ownerHtmlUrl":66,"ownerBlogUrl":67,"ownerDescription":68,"updatedAt":69,"install":70,"resources":83},"01M6AE3DDFEN33QYW0N0NTEYVM","conducting-memory-forensics-with-volatility",null,"Performs memory forensics analysis using Volatility 3 to extract evidence of malware execution, process injection, network connections, and credential theft from RAM dumps captured during incident response. Covers memory acquisition, process analysis, DLL inspection, and malware detection. Activates for requests involving memory forensics, RAM analysis, Volatility framework, memory dump investigation, volatile evidence analysis, or live memory acquisition.","mukul975\u002FAnthropic-Cybersecurity-Skills","mukul975","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F42860185?v=4","01M6ADEQR8T5M4EEG6C67BQCWF","Mahipal","01M6ADJPMYWG1E5GM1FWBE3128",36078,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fconducting-memory-forensics-with-volatility\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"memory","forensics","Volatility","conducting memory forensics with volatility","cybersecurity","incident response","framework","malware","memory-forensics","dfir","incident-response","RAM-analysis","network","T1059.001","process-injection","2026-11-10 10:12:30",false,"---\nname: conducting-memory-forensics-with-volatility\ndescription: 'Performs memory forensics analysis using Volatility 3 to extract evidence\n  of malware execution, process injection, network connections, and credential theft\n  from RAM dumps captured during incident response. Covers memory acquisition, process\n  analysis, DLL inspection, and malware detection. Activates for requests involving\n  memory forensics, RAM analysis, Volatility framework, memory dump investigation,\n  volatile evidence analysis, or live memory acquisition.\n\n  '\ndomain: cybersecurity\nsubdomain: incident-response\ntags:\n- memory-forensics\n- volatility\n- RAM-analysis\n- process-injection\n- DFIR\nmitre_attack:\n- T1055\n- T1003.001\n- T1014\n- T1059.001\n- T1620\nversion: 1.0.0\nauthor: mahipal\nlicense: Apache-2.0\nnist_csf:\n- RS.MA-01\n- RS.MA-02\n- RS.AN-03\n- RC.RP-01\n---\n\n# Conducting Memory Forensics with Volatility\n\n## When to Use\n\n- An endpoint has been contained during an active incident and volatile evidence must be preserved\n- EDR alerts suggest process injection or fileless malware that only exists in memory\n- Encryption keys need to be recovered from a ransomware-infected system before shutdown\n- Credential theft (Mimikatz, LSASS dumping) is suspected and evidence must be confirmed\n- A rootkit or kernel-level compromise is suspected and disk-based analysis is insufficient\n\n**Do not use** for analyzing disk images or file system artifacts; use disk forensics tools (Autopsy, FTK) for those tasks.\n\n## Prerequisites\n\n- Memory acquisition tool deployed or available: WinPmem, Magnet RAM Capture, DumpIt, or AVML (Linux)\n- Volatility 3 installed with Python 3.8+ and required symbol tables\n- Sufficient storage for memory dumps (equal to system RAM size, typically 8-64 GB)\n- YARA rules for malware detection in memory (Florian Roth's signature-base, custom rules)\n- Reference baseline of normal processes and DLLs for the OS version being analyzed\n- Chain of custody documentation for evidence handling\n\n## Workflow\n\n### Step 1: Acquire Memory Image\n\nCapture RAM from the target system using a forensically sound method:\n\n**Windows (WinPmem):**\n```\nwinpmem_mini_x64.exe output.raw\n```\n\n**Windows (Magnet RAM Capture):**\n```\nMagnetRAMCapture.exe\n# GUI-based, select output path, generates .raw file\n```\n\n**Windows (DumpIt):**\n```\nDumpIt.exe\n# Creates memory dump in current directory automatically\n```\n\n**Linux (AVML - Acquire Volatile Memory for Linux):**\n```\n.\u002Favml output.lime\n```\n\nDocument acquisition metadata:\n```\nAcquisition Record:\n━━━━━━━━━━━━━━━━━\nTarget Host:      WKSTN-042\nRAM Size:         16 GB\nDump File:        WKSTN-042_20251115_1445.raw\nDump Size:        16,843,612,160 bytes\nSHA-256:          a4b3c2d1e5f6...\nAcquisition Tool: WinPmem 4.0\nAcquired By:      [Analyst Name]\nTimestamp:        2025-11-15T14:45:00Z\n```\n\n### Step 2: Identify the Operating System and Profile\n\nVolatility 3 automatically identifies the OS, but verify:\n\n```bash\n# Get system information\nvol -f WKSTN-042_20251115_1445.raw windows.info\n\n# Output includes:\n# OS: Windows 10 22H2 (Build 19045.3693)\n# Kernel Base: 0xf8066c200000\n# DTB: 0x1aa000\n# Symbols: ntkrnlmp.pdb\n```\n\n### Step 3: Analyze Running Processes\n\nExamine the process tree for suspicious activity:\n\n```bash\n# List all running processes\nvol -f memory.raw windows.pslist\n\n# Show process tree (parent-child relationships)\nvol -f memory.raw windows.pstree\n\n# Scan for hidden\u002Funlinked processes (rootkit detection)\nvol -f memory.raw windows.psscan\n\n# Compare pslist vs psscan to find hidden processes\n# Processes in psscan but NOT in pslist may be hidden by rootkits\n```\n\nKey indicators of compromise in process analysis:\n- `svchost.exe` running without `-k` parameter or with wrong parent (should be `services.exe`)\n- `csrss.exe` or `lsass.exe` with abnormal parent process\n- Processes with misspelled names (`scvhost.exe`, `lssas.exe`)\n- Unusual processes spawned by `outlook.exe`, `winword.exe`, or `excel.exe`\n- Multiple instances of processes that should be singletons (`lsass.exe`, `smss.exe`)\n\n### Step 4: Investigate Network Connections\n\nExtract active and recently closed network connections:\n\n```bash\n# List all network connections\nvol -f memory.raw windows.netscan\n\n# Focus output fields:\n# Offset    Proto  LocalAddr     LocalPort  ForeignAddr    ForeignPort  State     PID  Owner\n# 0xe10...  TCPv4  10.1.5.42     49721     185.220.101.42  443         ESTAB     3847  update.exe\n```\n\nCross-reference suspicious connections with the process tree to identify C2 communications. Look for:\n- Connections to external IPs from unexpected processes\n- High port numbers connecting to port 443\u002F80 from non-browser processes\n- Connections from `svchost.exe` or system processes to external IPs\n\n### Step 5: Detect Process Injection and Malware\n\nUse malfind to identify injected code and memory-resident malware:\n\n```bash\n# Detect injected code in processes\nvol -f memory.raw windows.malfind\n\n# Output shows:\n# PID  Process       Start      End        Tag  Protection  Hexdump\u002FDisassembly\n# 3847 explorer.exe  0x2a10000  0x2a14000  VadS PAGE_EXECUTE_READWRITE\n# MZ header detected - injected PE\n\n# Dump suspicious process memory\nvol -f memory.raw windows.memmap --pid 3847 --dump\n\n# List DLLs loaded by a suspicious process\nvol -f memory.raw windows.dlllist --pid 3847\n\n# Scan memory with YARA rules\nvol -f memory.raw windows.yarascan --yara-file malware_rules.yar\n```\n\n### Step 6: Extract Credentials and Artifacts\n\nRecover sensitive data from memory:\n\n```bash\n# Dump registry hives from memory (for password hash extraction)\nvol -f memory.raw windows.registry.hivelist\nvol -f memory.raw windows.hashdump\n\n# Extract command line history\nvol -f memory.raw windows.cmdline\n\n# List handles (files, registry keys, mutexes)\nvol -f memory.raw windows.handles --pid 3847\n\n# Extract clipboard contents\nvol -f memory.raw windows.clipboard\n\n# Dump cached files from memory\nvol -f memory.raw windows.dumpfiles --pid 3847\n```\n\n### Step 7: Generate Forensic Report\n\nCompile findings into a structured analysis report documenting all evidence extracted from memory:\n\n- Process anomalies with PIDs, parent processes, and timestamps\n- Network connections with associated process context\n- Injected code regions with memory protection flags\n- Extracted IOCs (hashes, IPs, domains, mutexes, registry keys)\n- YARA rule matches with rule names and match offsets\n- Credential exposure (hashes found, accounts at risk)\n\n## Key Concepts\n\n| Term | Definition |\n|------|------------|\n| **Volatile Evidence** | Data that exists only in RAM and is lost when a system is powered off; includes running processes, network connections, encryption keys |\n| **Process Injection** | Technique where malware inserts code into a legitimate process's memory space to evade detection (malfind detects this) |\n| **EPROCESS** | Windows kernel data structure representing a process; psscan searches for these structures even when unlinked from the active process list |\n| **VAD (Virtual Address Descriptor)** | Windows kernel structure tracking memory regions allocated to a process; malfind examines VADs for executable but non-file-backed regions |\n| **Symbol Tables** | OS-specific data structures that Volatility 3 uses to parse memory; downloaded automatically based on detected OS version |\n| **PAGE_EXECUTE_READWRITE** | Memory protection flag indicating a region is readable, writable, and executable; common indicator of injected malicious code |\n| **Memory-Resident Malware** | Malware that operates entirely in RAM without writing persistent files to disk, making it invisible to traditional disk-based antivirus |\n\n## Tools & Systems\n\n- **Volatility 3**: Primary open-source memory forensics framework; Python 3 rewrite with automatic symbol resolution\n- **WinPmem \u002F DumpIt \u002F Magnet RAM Capture**: Memory acquisition tools for Windows systems\n- **AVML (Acquire Volatile Memory for Linux)**: Microsoft's open-source Linux memory acquisition tool\n- **YARA**: Pattern matching engine for scanning memory dumps against malware signatures and behavioral rules\n- **MemProcFS**: Memory analysis tool that presents memory as a virtual file system for intuitive browsing\n\n## Common Scenarios\n\n### Scenario: Detecting Cobalt Strike Beacon in Memory\n\n**Context**: EDR detects suspicious named pipe activity but cannot identify the source. A memory dump is acquired from the suspect endpoint for analysis.\n\n**Approach**:\n1. Run `windows.pstree` to identify the process hierarchy and spot abnormal parent-child relationships\n2. Run `windows.malfind` to detect injected code regions, particularly in `svchost.exe` or `rundll32.exe`\n3. Dump the injected memory region and scan with YARA rules for Cobalt Strike beacon signatures\n4. Run `windows.netscan` to identify C2 connections and correlate with the injected process PID\n5. Extract the beacon configuration (C2 URLs, sleep time, jitter, watermark) using CobaltStrikeParser\n6. Run `windows.cmdline` to identify any post-exploitation commands executed\n\n**Pitfalls**:\n- Analyzing only the process list without running malfind (missing injected code in legitimate processes)\n- Not capturing memory before isolating the endpoint (EDR containment may trigger malware self-deletion)\n- Using Volatility 2 profiles instead of Volatility 3 automatic symbol resolution on newer Windows versions\n\n## Output Format\n\n```\nMEMORY FORENSICS ANALYSIS REPORT\n==================================\nIncident:         INC-2025-1547\nEvidence File:    WKSTN-042_20251115_1445.raw\nSHA-256:          a4b3c2d1e5f6...\nOS Identified:    Windows 10 22H2 (Build 19045)\nAnalysis Tool:    Volatility 3.2.0\n\nPROCESS ANOMALIES\nPID    Process         Parent       Anomaly\n3847   update.exe      powershell   Suspicious executable in Temp directory\n5102   svchost.exe     explorer     Wrong parent (expected services.exe)\n---    [hidden]        ---          Found in psscan but not pslist\n\nINJECTED CODE\nPID    Process        Address Range        Protection              Finding\n5102   svchost.exe    0x00A10000-0x00A14   PAGE_EXECUTE_READWRITE  MZ header (PE injection)\n\nNETWORK CONNECTIONS\nPID    Process      Local              Foreign             State\n3847   update.exe   10.1.5.42:49721    185.220.101.42:443  ESTABLISHED\n5102   svchost.exe  10.1.5.42:51003    91.215.85.17:8443   ESTABLISHED\n\nYARA MATCHES\nRule: CobaltStrike_Beacon_x64\nMatch PID: 5102 (svchost.exe)\nOffset: 0x00A10240\n\nEXTRACTED IOCS\nHashes:     [SHA-256 of dumped injected code]\nC2 IPs:     185.220.101.42, 91.215.85.17\nC2 Domains: [extracted from beacon config]\nMutexes:    Global\\MSCTF.Shared.MUTEX.ZRQ\n```\n","# Conducting Memory Forensics with Volatility\n\n## When to Use\n\n- An endpoint has been contained during an active incident and volatile evidence must be preserved\n- EDR alerts suggest process injection or fileless malware that only exists in memory\n- Encryption keys need to be recovered from a ransomware-infected system before shutdown\n- Credential theft (Mimikatz, LSASS dumping) is suspected and evidence must be confirmed\n- A rootkit or kernel-level compromise is suspected and disk-based analysis is insufficient\n\n**Do not use** for analyzing disk images or file system artifacts; use disk forensics tools (Autopsy, FTK) for those tasks.\n\n## Prerequisites\n\n- Memory acquisition tool deployed or available: WinPmem, Magnet RAM Capture, DumpIt, or AVML (Linux)\n- Volatility 3 installed with Python 3.8+ and required symbol tables\n- Sufficient storage for memory dumps (equal to system RAM size, typically 8-64 GB)\n- YARA rules for malware detection in memory (Florian Roth's signature-base, custom rules)\n- Reference baseline of normal processes and DLLs for the OS version being analyzed\n- Chain of custody documentation for evidence handling\n\n## Workflow\n\n### Step 1: Acquire Memory Image\n\nCapture RAM from the target system using a forensically sound method:\n\n**Windows (WinPmem):**\n```\nwinpmem_mini_x64.exe output.raw\n```\n\n**Windows (Magnet RAM Capture):**\n```\nMagnetRAMCapture.exe\n# GUI-based, select output path, generates .raw file\n```\n\n**Windows (DumpIt):**\n```\nDumpIt.exe\n# Creates memory dump in current directory automatically\n```\n\n**Linux (AVML - Acquire Volatile Memory for Linux):**\n```\n.\u002Favml output.lime\n```\n\nDocument acquisition metadata:\n```\nAcquisition Record:\n━━━━━━━━━━━━━━━━━\nTarget Host:      WKSTN-042\nRAM Size:         16 GB\nDump File:        WKSTN-042_20251115_1445.raw\nDump Size:        16,843,612,160 bytes\nSHA-256:          a4b3c2d1e5f6...\nAcquisition Tool: WinPmem 4.0\nAcquired By:      [Analyst Name]\nTimestamp:        2025-11-15T14:45:00Z\n```\n\n### Step 2: Identify the Operating System and Profile\n\nVolatility 3 automatically identifies the OS, but verify:\n\n```bash\n# Get system information\nvol -f WKSTN-042_20251115_1445.raw windows.info\n\n# Output includes:\n# OS: Windows 10 22H2 (Build 19045.3693)\n# Kernel Base: 0xf8066c200000\n# DTB: 0x1aa000\n# Symbols: ntkrnlmp.pdb\n```\n\n### Step 3: Analyze Running Processes\n\nExamine the process tree for suspicious activity:\n\n```bash\n# List all running processes\nvol -f memory.raw windows.pslist\n\n# Show process tree (parent-child relationships)\nvol -f memory.raw windows.pstree\n\n# Scan for hidden\u002Funlinked processes (rootkit detection)\nvol -f memory.raw windows.psscan\n\n# Compare pslist vs psscan to find hidden processes\n# Processes in psscan but NOT in pslist may be hidden by rootkits\n```\n\nKey indicators of compromise in process analysis:\n- `svchost.exe` running without `-k` parameter or with wrong parent (should be `services.exe`)\n- `csrss.exe` or `lsass.exe` with abnormal parent process\n- Processes with misspelled names (`scvhost.exe`, `lssas.exe`)\n- Unusual processes spawned by `outlook.exe`, `winword.exe`, or `excel.exe`\n- Multiple instances of processes that should be singletons (`lsass.exe`, `smss.exe`)\n\n### Step 4: Investigate Network Connections\n\nExtract active and recently closed network connections:\n\n```bash\n# List all network connections\nvol -f memory.raw windows.netscan\n\n# Focus output fields:\n# Offset    Proto  LocalAddr     LocalPort  ForeignAddr    ForeignPort  State     PID  Owner\n# 0xe10...  TCPv4  10.1.5.42     49721     185.220.101.42  443         ESTAB     3847  update.exe\n```\n\nCross-reference suspicious connections with the process tree to identify C2 communications. Look for:\n- Connections to external IPs from unexpected processes\n- High port numbers connecting to port 443\u002F80 from non-browser processes\n- Connections from `svchost.exe` or system processes to external IPs\n\n### Step 5: Detect Process Injection and Malware\n\nUse malfind to identify injected code and memory-resident malware:\n\n```bash\n# Detect injected code in processes\nvol -f memory.raw windows.malfind\n\n# Output shows:\n# PID  Process       Start      End        Tag  Protection  Hexdump\u002FDisassembly\n# 3847 explorer.exe  0x2a10000  0x2a14000  VadS PAGE_EXECUTE_READWRITE\n# MZ header detected - injected PE\n\n# Dump suspicious process memory\nvol -f memory.raw windows.memmap --pid 3847 --dump\n\n# List DLLs loaded by a suspicious process\nvol -f memory.raw windows.dlllist --pid 3847\n\n# Scan memory with YARA rules\nvol -f memory.raw windows.yarascan --yara-file malware_rules.yar\n```\n\n### Step 6: Extract Credentials and Artifacts\n\nRecover sensitive data from memory:\n\n```bash\n# Dump registry hives from memory (for password hash extraction)\nvol -f memory.raw windows.registry.hivelist\nvol -f memory.raw windows.hashdump\n\n# Extract command line history\nvol -f memory.raw windows.cmdline\n\n# List handles (files, registry keys, mutexes)\nvol -f memory.raw windows.handles --pid 3847\n\n# Extract clipboard contents\nvol -f memory.raw windows.clipboard\n\n# Dump cached files from memory\nvol -f memory.raw windows.dumpfiles --pid 3847\n```\n\n### Step 7: Generate Forensic Report\n\nCompile findings into a structured analysis report documenting all evidence extracted from memory:\n\n- Process anomalies with PIDs, parent processes, and timestamps\n- Network connections with associated process context\n- Injected code regions with memory protection flags\n- Extracted IOCs (hashes, IPs, domains, mutexes, registry keys)\n- YARA rule matches with rule names and match offsets\n- Credential exposure (hashes found, accounts at risk)\n\n## Key Concepts\n\n| Term | Definition |\n|------|------------|\n| **Volatile Evidence** | Data that exists only in RAM and is lost when a system is powered off; includes running processes, network connections, encryption keys |\n| **Process Injection** | Technique where malware inserts code into a legitimate process's memory space to evade detection (malfind detects this) |\n| **EPROCESS** | Windows kernel data structure representing a process; psscan searches for these structures even when unlinked from the active process list |\n| **VAD (Virtual Address Descriptor)** | Windows kernel structure tracking memory regions allocated to a process; malfind examines VADs for executable but non-file-backed regions |\n| **Symbol Tables** | OS-specific data structures that Volatility 3 uses to parse memory; downloaded automatically based on detected OS version |\n| **PAGE_EXECUTE_READWRITE** | Memory protection flag indicating a region is readable, writable, and executable; common indicator of injected malicious code |\n| **Memory-Resident Malware** | Malware that operates entirely in RAM without writing persistent files to disk, making it invisible to traditional disk-based antivirus |\n\n## Tools & Systems\n\n- **Volatility 3**: Primary open-source memory forensics framework; Python 3 rewrite with automatic symbol resolution\n- **WinPmem \u002F DumpIt \u002F Magnet RAM Capture**: Memory acquisition tools for Windows systems\n- **AVML (Acquire Volatile Memory for Linux)**: Microsoft's open-source Linux memory acquisition tool\n- **YARA**: Pattern matching engine for scanning memory dumps against malware signatures and behavioral rules\n- **MemProcFS**: Memory analysis tool that presents memory as a virtual file system for intuitive browsing\n\n## Common Scenarios\n\n### Scenario: Detecting Cobalt Strike Beacon in Memory\n\n**Context**: EDR detects suspicious named pipe activity but cannot identify the source. A memory dump is acquired from the suspect endpoint for analysis.\n\n**Approach**:\n1. Run `windows.pstree` to identify the process hierarchy and spot abnormal parent-child relationships\n2. Run `windows.malfind` to detect injected code regions, particularly in `svchost.exe` or `rundll32.exe`\n3. Dump the injected memory region and scan with YARA rules for Cobalt Strike beacon signatures\n4. Run `windows.netscan` to identify C2 connections and correlate with the injected process PID\n5. Extract the beacon configuration (C2 URLs, sleep time, jitter, watermark) using CobaltStrikeParser\n6. Run `windows.cmdline` to identify any post-exploitation commands executed\n\n**Pitfalls**:\n- Analyzing only the process list without running malfind (missing injected code in legitimate processes)\n- Not capturing memory before isolating the endpoint (EDR containment may trigger malware self-deletion)\n- Using Volatility 2 profiles instead of Volatility 3 automatic symbol resolution on newer Windows versions\n\n## Output Format\n\n```\nMEMORY FORENSICS ANALYSIS REPORT\n==================================\nIncident:         INC-2025-1547\nEvidence File:    WKSTN-042_20251115_1445.raw\nSHA-256:          a4b3c2d1e5f6...\nOS Identified:    Windows 10 22H2 (Build 19045)\nAnalysis Tool:    Volatility 3.2.0\n\nPROCESS ANOMALIES\nPID    Process         Parent       Anomaly\n3847   update.exe      powershell   Suspicious executable in Temp directory\n5102   svchost.exe     explorer     Wrong parent (expected services.exe)\n---    [hidden]        ---          Found in psscan but not pslist\n\nINJECTED CODE\nPID    Process        Address Range        Protection              Finding\n5102   svchost.exe    0x00A10000-0x00A14   PAGE_EXECUTE_READWRITE  MZ header (PE injection)\n\nNETWORK CONNECTIONS\nPID    Process      Local              Foreign             State\n3847   update.exe   10.1.5.42:49721    185.220.101.42:443  ESTABLISHED\n5102   svchost.exe  10.1.5.42:51003    91.215.85.17:8443   ESTABLISHED\n\nYARA MATCHES\nRule: CobaltStrike_Beacon_x64\nMatch PID: 5102 (svchost.exe)\nOffset: 0x00A10240\n\nEXTRACTED IOCS\nHashes:     [SHA-256 of dumped injected code]\nC2 IPs:     185.220.101.42, 91.215.85.17\nC2 Domains: [extracted from beacon config]\nMutexes:    Global\\MSCTF.Shared.MUTEX.ZRQ\n```\n",{"name":5,"tags":44,"author":47,"domain":28,"license":15,"version":48,"nist_csf":49,"subdomain":34,"description":54,"mitre_attack":55},[32,45,35,38,46],"volatility","DFIR","mahipal","1.0.0",[50,51,52,53],"RS.MA-01","RS.MA-02","RS.AN-03","RC.RP-01","Performs memory forensics analysis using Volatility 3 to extract evidence of malware execution, process injection, network connections, and credential theft from RAM dumps captured during incident response. Covers memory acquisition, process analysis, DLL inspection, and malware detection. Activates for requests involving memory forensics, RAM analysis, Volatility framework, memory dump investigation, volatile evidence analysis, or live memory acquisition.\n",[56,57,58,37,59],"T1055","T1003.001","T1014","T1620","main","54a798831d2266a3ca61ce68a7acb80b81160d57","skills\u002Fconducting-memory-forensics-with-volatility",4414,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills","https:\u002F\u002Fmahipal.engineer\u002FAnthropic-Cybersecurity-Skills\u002F","https:\u002F\u002Fgithub.com\u002Fmukul975","Mahipal.engineer","🔐 Cybersecurity | 💻 Dev | 📷 Street Photographer\r\n🎓 MSc | 📚 Research & AI Security","2026-08-31 12:32:44",{"source":8,"skillName":5,"commands":71,"prompt":81,"zipAvailable":82},[72,75,78],{"tool":73,"command":74},"npx","npx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill conducting-memory-forensics-with-volatility",{"tool":76,"command":77},"bunx","bunx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill conducting-memory-forensics-with-volatility",{"tool":79,"command":80},"pnpm","pnpm dlx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill conducting-memory-forensics-with-volatility","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fconducting-memory-forensics-with-volatility\u002FSKILL.md",true,[84,89,95],{"path":85,"resourceType":85,"resourceTypeDesc":86,"sizeBytes":87,"sourceHtmlUrl":88},"LICENSE","许可证",11255,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fconducting-memory-forensics-with-volatility\u002FLICENSE",{"path":90,"resourceType":91,"resourceTypeDesc":92,"sizeBytes":93,"sourceHtmlUrl":94},"references\u002Fapi-reference.md","REFERENCE","参考文档",2361,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fconducting-memory-forensics-with-volatility\u002Freferences\u002Fapi-reference.md",{"path":96,"resourceType":97,"resourceTypeDesc":98,"sizeBytes":99,"sourceHtmlUrl":100},"scripts\u002Fagent.py","SCRIPT","脚本",6788,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fconducting-memory-forensics-with-volatility\u002Fscripts\u002Fagent.py",[102,128,152,179,204,230],{"skillNo":103,"name":104,"nameZh":6,"description":105,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":106,"categories":107,"keywords":109,"publishedAt":39},"01M6AGDM3HJXTKXX15ZZ0ZVCJ3","verifying-build-provenance-with-slsa-sigstore","Verifies artifact signatures and SLSA provenance using Sigstore's cosign (verify, verify-attestation, verify-blob-attestation) and slsa-verifier (verify-artifact), enforcing keyless OIDC builder identity and source repo against SLSA Build levels. Use in CI\u002FCD before deploying artifacts, when consuming third-party attestations, establishing a SLSA Build L3 pipeline, or confirming provenance during incident response or admission control.","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fverifying-build-provenance-with-slsa-sigstore\u002FSKILL.md",[108],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[110,111,112,113,114,28,29,115,116,117,118,119,120,121,122,123,124,125,126,127],"sigstore","provenance","slsa","verifying build provenance with slsa sigstore","security","identity","pr","code","artifacts","ci","supply-chain","pipeline","supply-chain-security","t1195","attestation","code-signing","cosign","keyless",{"skillNo":129,"name":130,"nameZh":6,"description":131,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":132,"categories":133,"keywords":135,"publishedAt":39},"01M6AGDGBPWYVZ2F11GFKJ4HZ9","validating-tpm-measured-boot-attestation","Verifies TPM 2.0 measured-boot integrity and remote attestation with tpm2-tools -- reading PCRs (tpm2_pcrread), replaying the boot event log, generating and checking signed quotes (tpm2_quote\u002Ftpm2_checkquote), and sealing secrets to a PCR policy. Use to confirm a system booted trusted firmware\u002Fkernel for Zero Trust device posture, detect boot-chain tampering via PCR drift, or build a golden-value baseline for fleet attestation.","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fvalidating-tpm-measured-boot-attestation\u002FSKILL.md",[134],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[136,124,137,138,114,139,28,116,140,141,142,143,144,145,146,147,148,149,150,151],"boot","TPM","validating tpm measured boot attestation","policy","verification","ZeRO","firmware","Tools","measured-boot","PCR","integrity","secrets","remote-attestation","tpm2-tools","integrity-verification","trusted-computing",{"skillNo":153,"name":154,"nameZh":6,"description":155,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":156,"categories":157,"keywords":162,"publishedAt":39},"01M6AGDCW0Y4FXZ3362GT1941M","validating-backup-integrity-for-recovery","Validates backup integrity through cryptographic hash verification, automated restore testing, corruption detection, and recoverability checks to confirm backups are reliable for disaster recovery and ransomware response. Use before relying on backups for recovery, when building post-backup validation pipelines, auditing backup infrastructure for compliance, or checking immutable\u002Fair-gapped backups for silent corruption or tampering.","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fvalidating-backup-integrity-for-recovery\u002FSKILL.md",[158,161],{"categoryNo":159,"categoryName":160,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[146,163,164,165,166,28,167,168,140,169,170,34,171,172,173,174,175,176,177,178],"backup","recovery","validating backup integrity for recovery","testing","infrastructure","validation","compliance","hash-verification","ransomware","cryptocurrency","detection","t1078","t1490","immutable","disaster-recovery","restore-testing",{"skillNo":180,"name":181,"nameZh":6,"description":182,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":183,"categories":184,"keywords":186,"publishedAt":39},"01M6AGD9GBG2KZ336AE763J3WH","triaging-windows-with-kape","Runs KAPE (Kroll Artifact Parser and Extractor) to collect targeted forensic artifacts (registry hives, $MFT, event logs, prefetch, browser data) via Targets and parse them with Modules wrapping Eric Zimmerman's EZ Tools (PECmd, MFTECmd, RECmd). Use during early incident containment\u002Ftriage when full disk imaging is impractical but a defensible, parseable Windows artifact set is needed quickly, including at-scale remote collection.","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Ftriaging-windows-with-kape\u002FSKILL.md",[185],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[187,188,189,28,190,191,192,193,33,194,195,196,197,198,199,200,143,201,202,203],"windows","kape","triaging windows with kape","registry","browser","logs","windows-forensics","mft","mftecmd","data","prefetch","eric-zimmerman","digital-forensics","SET","collection","pecmd","artifact-collection",{"skillNo":205,"name":206,"nameZh":6,"description":207,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":208,"categories":209,"keywords":211,"publishedAt":39},"01M6AGD5PW5J4EVZM958WD74ZQ","triaging-vulnerabilities-with-ssvc-framework","Triages and prioritizes vulnerabilities with CISA's Stakeholder-Specific Vulnerability Categorization (SSVC) decision tree, weighing exploitation status (via the CISA KEV catalog and FIRST EPSS API), technical impact, automatability, and mission prevalence to output Track\u002FTrack*\u002FAttend\u002FAct decisions. Use when prioritizing vulnerability scan results (OpenVAS, Nessus, Qualys) for remediation planning beyond raw CVSS scores.","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Ftriaging-vulnerabilities-with-ssvc-framework\u002FSKILL.md",[210],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[212,30,213,28,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229],"vulnerabilities","triaging vulnerabilities with ssvc framework","status","planning","CISA","vulnerability-management","risk-management","nessus","qualys","openvas","cvss","remediation","risk","vulnerability","api","prioritization","ssvc","decision-tree",{"skillNo":231,"name":232,"nameZh":6,"description":233,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":234,"categories":235,"keywords":237,"publishedAt":39},"01M6AGD24TTQQM4CXG69E3B9TJ","triaging-security-incident","Performs initial triage of security incidents using the NIST SP 800-61r3 and SANS PICERL frameworks, classifying incident type, assigning priority by business impact, and routing to the appropriate response team. Use when a SIEM\u002FEDR alert needs human classification, concurrent alerts must be prioritized, or a user report or threat-intel IOC match requires initial incident categorization.","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Ftriaging-security-incident\u002FSKILL.md",[236],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[114,238,28,239,240,241,242,243,244,245,246,174,247,175,248,249,250,251,252,253],"triaging security incident","siem","file","ioc","soc","NIST-800-61","NIST","severity","edr","execution","analysis","classification","severity-classification","incident-triage","SANS-PICERL","SOC-operations"]