[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6ADP6JS5JH6MVTBT9TJ2W2C":3,"skill-same:01M6ADP6JS5JH6MVTBT9TJ2W2C":90},{"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":18,"publishedAt":32,"linkOnly":33,"markdownContent":34,"markdownBody":35,"frontMatter":36,"refName":49,"commitSha":50,"skillDirectory":51,"forksCount":52,"repositoryHtmlUrl":53,"repositoryHomepageUrl":54,"ownerHtmlUrl":55,"ownerBlogUrl":56,"ownerDescription":57,"updatedAt":58,"install":59,"resources":72},"01M6ADP6JS5JH6MVTBT9TJ2W2C","analyzing-linux-kernel-rootkits",null,"Detect kernel-level rootkits in Linux memory dumps using Volatility3 linux plugins (check_syscall, lsmod, hidden_modules), rkhunter system scanning, and \u002Fproc vs \u002Fsys discrepancy analysis to identify hooked syscalls, hidden kernel modules, and tampered system structures.","mukul975\u002FAnthropic-Cybersecurity-Skills","mukul975","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F42860185?v=4","01M6ADEQR8T5M4EEG6C67BQCWF","Mahipal","01M6ADJPMYWG1E5GM1FWBE3128",35884,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fanalyzing-linux-kernel-rootkits\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31],"kernel","linux","analyzing linux kernel rootkits","memory","cybersecurity","plugins","forensics","malware","rootkit","malware-analysis","memory-forensics","volatility3","rkhunter","2026-11-10 10:12:30",false,"---\nname: analyzing-linux-kernel-rootkits\ndescription: Detect kernel-level rootkits in Linux memory dumps using Volatility3\n  linux plugins (check_syscall, lsmod, hidden_modules), rkhunter system scanning,\n  and \u002Fproc vs \u002Fsys discrepancy analysis to identify hooked syscalls, hidden kernel\n  modules, and tampered system structures.\ndomain: cybersecurity\nsubdomain: digital-forensics\ntags:\n- rootkit\n- linux\n- kernel\n- volatility3\n- memory-forensics\n- malware-analysis\n- rkhunter\n- forensics\nversion: '1.0'\nauthor: mahipal\nlicense: Apache-2.0\nnist_csf:\n- RS.AN-03\n- DE.AE-02\n- RS.MA-01\nmitre_attack:\n- T1014\n- T1547.006\n- T1564.001\n---\n\n# Analyzing Linux Kernel Rootkits\n\n## Overview\n\nLinux kernel rootkits operate at ring 0, modifying kernel data structures to hide processes, files, network connections, and kernel modules from userspace tools. Detection requires either memory forensics (analyzing physical memory dumps with Volatility3) or cross-view analysis (comparing \u002Fproc, \u002Fsys, and kernel data structures for inconsistencies). This skill covers using Volatility3 Linux plugins to detect syscall table hooks, hidden kernel modules, and modified function pointers, supplemented by live system scanning with rkhunter and chkrootkit.\n\n\n## When to Use\n\n- When investigating security incidents that require analyzing linux kernel rootkits\n- When building detection rules or threat hunting queries for this domain\n- When SOC analysts need structured procedures for this analysis type\n- When validating security monitoring coverage for related attack techniques\n\n## Prerequisites\n\n- Volatility3 installed (pip install volatility3)\n- Linux memory dump (acquired via LiME, AVML, or \u002Fproc\u002Fkcore)\n- Volatility3 Linux symbol table (ISF) matching the target kernel version\n- rkhunter and chkrootkit for live system scanning\n- Reference known-good kernel image for comparison\n\n## Steps\n\n### Step 1: Acquire Memory Dump\nCapture Linux physical memory using LiME kernel module or AVML for cloud instances.\n\n### Step 2: Analyze with Volatility3\nRun linux.check_syscall, linux.lsmod, linux.hidden_modules, and linux.check_idt plugins to detect rootkit artifacts.\n\n### Step 3: Cross-View Analysis\nCompare module lists from \u002Fproc\u002Fmodules, lsmod, and \u002Fsys\u002Fmodule to identify modules hidden from one view but present in another.\n\n### Step 4: Live System Scanning\nRun rkhunter and chkrootkit to detect known rootkit signatures, suspicious files, and modified system binaries.\n\n## Expected Output\n\nJSON report containing detected syscall hooks, hidden kernel modules, modified IDT entries, suspicious \u002Fproc discrepancies, and rkhunter findings.\n\n## Example Output\n\n```text\n$ sudo python3 rootkit_analyzer.py --memory \u002Fevidence\u002Flinux-mem.lime --profile Ubuntu2204\n\nLinux Kernel Rootkit Analysis Report\n=====================================\nMemory Image: \u002Fevidence\u002Flinux-mem.lime\nKernel Version: 5.15.0-91-generic (Ubuntu 22.04 LTS)\nAnalysis Time: 2024-01-18 09:15:32 UTC\n\n[+] Scanning syscall table for hooks...\n    Syscall Table Base: 0xffffffff82200300\n    Total syscalls checked: 449\n\n    HOOKED SYSCALLS DETECTED:\n    ┌─────────┬──────────────────┬──────────────────────┬──────────────────────┐\n    │ NR      │ Syscall          │ Expected Address     │ Current Address      │\n    ├─────────┼──────────────────┼──────────────────────┼──────────────────────┤\n    │ 0       │ sys_read         │ 0xffffffff8139a0e0   │ 0xffffffffc0a12000   │\n    │ 2       │ sys_open         │ 0xffffffff8139b340   │ 0xffffffffc0a12180   │\n    │ 78      │ sys_getdents64   │ 0xffffffff813f5210   │ 0xffffffffc0a12300   │\n    │ 62      │ sys_kill         │ 0xffffffff8110c4a0   │ 0xffffffffc0a12480   │\n    └─────────┴──────────────────┴──────────────────────┴──────────────────────┘\n    WARNING: 4 syscall hooks detected - rootkit behavior confirmed\n\n[+] Checking for hidden kernel modules...\n    Loaded modules (lsmod):         147\n    Modules in kobject list:        149\n    HIDDEN MODULES:\n      - \"netfilter_helper\" at 0xffffffffc0a10000 (size: 12288)\n      - \"kworker_sched\"    at 0xffffffffc0a14000 (size: 8192)\n\n[+] Scanning \u002Fproc for discrepancies...\n    Processes in task_struct list: 234\n    Processes visible in \u002Fproc:   231\n    HIDDEN PROCESSES:\n      - PID 31337  cmd: \"[kworker\u002F0:3]\"   (disguised as kernel thread)\n      - PID 31442  cmd: \"rsyslogd\"         (fake, real rsyslogd is PID 892)\n      - PID 31500  cmd: \"\"                 (unnamed process)\n\n[+] Checking IDT entries...\n    IDT entries scanned: 256\n    Modified entries: 0 (clean)\n\n[+] Running rkhunter scan...\n    Checking for known rootkits:        68 variants checked\n    Diamorphine rootkit:                WARNING - signatures match\n    System binary checks:\n      \u002Fusr\u002Fbin\u002Fps:     MODIFIED (SHA-256 mismatch)\n      \u002Fusr\u002Fbin\u002Fnetstat: MODIFIED (SHA-256 mismatch)\n      \u002Fusr\u002Fbin\u002Fls:     MODIFIED (SHA-256 mismatch)\n      \u002Fusr\u002Fsbin\u002Fss:    OK\n\n[+] Network analysis...\n    Hidden connections (not in \u002Fproc\u002Fnet\u002Ftcp):\n      ESTABLISHED  0.0.0.0:0 -> 198.51.100.47:4443 (PID 31337)\n      ESTABLISHED  0.0.0.0:0 -> 198.51.100.47:8080 (PID 31442)\n\nSummary:\n  Rootkit Type:         Loadable Kernel Module (LKM)\n  Probable Family:      Diamorphine variant\n  Syscall Hooks:        4 (read, open, getdents64, kill)\n  Hidden Modules:       2\n  Hidden Processes:     3\n  Hidden Connections:   2 (C2: 198.51.100.47)\n  Modified Binaries:    3 (\u002Fusr\u002Fbin\u002Fps, netstat, ls)\n  Risk Level:           CRITICAL\n```\n","# Analyzing Linux Kernel Rootkits\n\n## Overview\n\nLinux kernel rootkits operate at ring 0, modifying kernel data structures to hide processes, files, network connections, and kernel modules from userspace tools. Detection requires either memory forensics (analyzing physical memory dumps with Volatility3) or cross-view analysis (comparing \u002Fproc, \u002Fsys, and kernel data structures for inconsistencies). This skill covers using Volatility3 Linux plugins to detect syscall table hooks, hidden kernel modules, and modified function pointers, supplemented by live system scanning with rkhunter and chkrootkit.\n\n\n## When to Use\n\n- When investigating security incidents that require analyzing linux kernel rootkits\n- When building detection rules or threat hunting queries for this domain\n- When SOC analysts need structured procedures for this analysis type\n- When validating security monitoring coverage for related attack techniques\n\n## Prerequisites\n\n- Volatility3 installed (pip install volatility3)\n- Linux memory dump (acquired via LiME, AVML, or \u002Fproc\u002Fkcore)\n- Volatility3 Linux symbol table (ISF) matching the target kernel version\n- rkhunter and chkrootkit for live system scanning\n- Reference known-good kernel image for comparison\n\n## Steps\n\n### Step 1: Acquire Memory Dump\nCapture Linux physical memory using LiME kernel module or AVML for cloud instances.\n\n### Step 2: Analyze with Volatility3\nRun linux.check_syscall, linux.lsmod, linux.hidden_modules, and linux.check_idt plugins to detect rootkit artifacts.\n\n### Step 3: Cross-View Analysis\nCompare module lists from \u002Fproc\u002Fmodules, lsmod, and \u002Fsys\u002Fmodule to identify modules hidden from one view but present in another.\n\n### Step 4: Live System Scanning\nRun rkhunter and chkrootkit to detect known rootkit signatures, suspicious files, and modified system binaries.\n\n## Expected Output\n\nJSON report containing detected syscall hooks, hidden kernel modules, modified IDT entries, suspicious \u002Fproc discrepancies, and rkhunter findings.\n\n## Example Output\n\n```text\n$ sudo python3 rootkit_analyzer.py --memory \u002Fevidence\u002Flinux-mem.lime --profile Ubuntu2204\n\nLinux Kernel Rootkit Analysis Report\n=====================================\nMemory Image: \u002Fevidence\u002Flinux-mem.lime\nKernel Version: 5.15.0-91-generic (Ubuntu 22.04 LTS)\nAnalysis Time: 2024-01-18 09:15:32 UTC\n\n[+] Scanning syscall table for hooks...\n    Syscall Table Base: 0xffffffff82200300\n    Total syscalls checked: 449\n\n    HOOKED SYSCALLS DETECTED:\n    ┌─────────┬──────────────────┬──────────────────────┬──────────────────────┐\n    │ NR      │ Syscall          │ Expected Address     │ Current Address      │\n    ├─────────┼──────────────────┼──────────────────────┼──────────────────────┤\n    │ 0       │ sys_read         │ 0xffffffff8139a0e0   │ 0xffffffffc0a12000   │\n    │ 2       │ sys_open         │ 0xffffffff8139b340   │ 0xffffffffc0a12180   │\n    │ 78      │ sys_getdents64   │ 0xffffffff813f5210   │ 0xffffffffc0a12300   │\n    │ 62      │ sys_kill         │ 0xffffffff8110c4a0   │ 0xffffffffc0a12480   │\n    └─────────┴──────────────────┴──────────────────────┴──────────────────────┘\n    WARNING: 4 syscall hooks detected - rootkit behavior confirmed\n\n[+] Checking for hidden kernel modules...\n    Loaded modules (lsmod):         147\n    Modules in kobject list:        149\n    HIDDEN MODULES:\n      - \"netfilter_helper\" at 0xffffffffc0a10000 (size: 12288)\n      - \"kworker_sched\"    at 0xffffffffc0a14000 (size: 8192)\n\n[+] Scanning \u002Fproc for discrepancies...\n    Processes in task_struct list: 234\n    Processes visible in \u002Fproc:   231\n    HIDDEN PROCESSES:\n      - PID 31337  cmd: \"[kworker\u002F0:3]\"   (disguised as kernel thread)\n      - PID 31442  cmd: \"rsyslogd\"         (fake, real rsyslogd is PID 892)\n      - PID 31500  cmd: \"\"                 (unnamed process)\n\n[+] Checking IDT entries...\n    IDT entries scanned: 256\n    Modified entries: 0 (clean)\n\n[+] Running rkhunter scan...\n    Checking for known rootkits:        68 variants checked\n    Diamorphine rootkit:                WARNING - signatures match\n    System binary checks:\n      \u002Fusr\u002Fbin\u002Fps:     MODIFIED (SHA-256 mismatch)\n      \u002Fusr\u002Fbin\u002Fnetstat: MODIFIED (SHA-256 mismatch)\n      \u002Fusr\u002Fbin\u002Fls:     MODIFIED (SHA-256 mismatch)\n      \u002Fusr\u002Fsbin\u002Fss:    OK\n\n[+] Network analysis...\n    Hidden connections (not in \u002Fproc\u002Fnet\u002Ftcp):\n      ESTABLISHED  0.0.0.0:0 -> 198.51.100.47:4443 (PID 31337)\n      ESTABLISHED  0.0.0.0:0 -> 198.51.100.47:8080 (PID 31442)\n\nSummary:\n  Rootkit Type:         Loadable Kernel Module (LKM)\n  Probable Family:      Diamorphine variant\n  Syscall Hooks:        4 (read, open, getdents64, kill)\n  Hidden Modules:       2\n  Hidden Processes:     3\n  Hidden Connections:   2 (C2: 198.51.100.47)\n  Modified Binaries:    3 (\u002Fusr\u002Fbin\u002Fps, netstat, ls)\n  Risk Level:           CRITICAL\n```\n",{"name":5,"tags":37,"author":38,"domain":23,"license":15,"version":39,"nist_csf":40,"subdomain":44,"description":7,"mitre_attack":45},[27,20,19,30,29,28,31,25],"mahipal","1.0",[41,42,43],"RS.AN-03","DE.AE-02","RS.MA-01","digital-forensics",[46,47,48],"T1014","T1547.006","T1564.001","main","54a798831d2266a3ca61ce68a7acb80b81160d57","skills\u002Fanalyzing-linux-kernel-rootkits",4379,"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":60,"prompt":70,"zipAvailable":71},[61,64,67],{"tool":62,"command":63},"npx","npx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill analyzing-linux-kernel-rootkits",{"tool":65,"command":66},"bunx","bunx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill analyzing-linux-kernel-rootkits",{"tool":68,"command":69},"pnpm","pnpm dlx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill analyzing-linux-kernel-rootkits","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fanalyzing-linux-kernel-rootkits\u002FSKILL.md",true,[73,78,84],{"path":74,"resourceType":74,"resourceTypeDesc":75,"sizeBytes":76,"sourceHtmlUrl":77},"LICENSE","许可证",11255,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fanalyzing-linux-kernel-rootkits\u002FLICENSE",{"path":79,"resourceType":80,"resourceTypeDesc":81,"sizeBytes":82,"sourceHtmlUrl":83},"references\u002Fapi-reference.md","REFERENCE","参考文档",2592,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fanalyzing-linux-kernel-rootkits\u002Freferences\u002Fapi-reference.md",{"path":85,"resourceType":86,"resourceTypeDesc":87,"sizeBytes":88,"sourceHtmlUrl":89},"scripts\u002Fagent.py","SCRIPT","脚本",7024,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fanalyzing-linux-kernel-rootkits\u002Fscripts\u002Fagent.py",[91,122,146,174,199,226],{"skillNo":92,"name":93,"nameZh":6,"description":94,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":95,"categories":96,"keywords":102,"publishedAt":32},"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",[97],{"categoryNo":98,"categoryName":99,"taxonomyType":100,"taxonomyTypeDesc":101},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[103,104,105,106,107,23,108,109,110,111,112,113,114,115,116,117,118,119,120,121],"sigstore","provenance","slsa","verifying build provenance with slsa sigstore","security","incident response","identity","pr","code","artifacts","ci","supply-chain","pipeline","supply-chain-security","t1195","attestation","code-signing","cosign","keyless",{"skillNo":123,"name":124,"nameZh":6,"description":125,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":126,"categories":127,"keywords":129,"publishedAt":32},"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",[128],{"categoryNo":98,"categoryName":99,"taxonomyType":100,"taxonomyTypeDesc":101},[130,118,131,132,107,133,23,110,134,135,136,137,138,139,140,141,142,143,144,145],"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":147,"name":148,"nameZh":6,"description":149,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":150,"categories":151,"keywords":156,"publishedAt":32},"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",[152,155],{"categoryNo":153,"categoryName":154,"taxonomyType":100,"taxonomyTypeDesc":101},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":98,"categoryName":99,"taxonomyType":100,"taxonomyTypeDesc":101},[140,157,158,159,160,23,161,162,134,163,164,165,166,167,168,169,170,171,172,173],"backup","recovery","validating backup integrity for recovery","testing","infrastructure","validation","compliance","hash-verification","incident-response","ransomware","cryptocurrency","detection","t1078","t1490","immutable","disaster-recovery","restore-testing",{"skillNo":175,"name":176,"nameZh":6,"description":177,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":178,"categories":179,"keywords":181,"publishedAt":32},"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",[180],{"categoryNo":98,"categoryName":99,"taxonomyType":100,"taxonomyTypeDesc":101},[182,183,184,23,185,186,187,188,189,190,191,192,193,194,44,195,137,196,197,198],"windows","kape","triaging windows with kape","registry","browser","logs","windows-forensics","dfir","mft","mftecmd","data","prefetch","eric-zimmerman","SET","collection","pecmd","artifact-collection",{"skillNo":200,"name":201,"nameZh":6,"description":202,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":203,"categories":204,"keywords":206,"publishedAt":32},"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",[205],{"categoryNo":98,"categoryName":99,"taxonomyType":100,"taxonomyTypeDesc":101},[207,208,209,23,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225],"vulnerabilities","framework","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":227,"name":228,"nameZh":6,"description":229,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":230,"categories":231,"keywords":233,"publishedAt":32},"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",[232],{"categoryNo":98,"categoryName":99,"taxonomyType":100,"taxonomyTypeDesc":101},[107,234,23,235,236,237,238,239,240,241,242,169,243,170,244,245,246,247,248,249],"triaging security incident","siem","file","ioc","soc","NIST-800-61","NIST","severity","edr","execution","analysis","classification","severity-classification","incident-triage","SANS-PICERL","SOC-operations"]