[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6AEMJR7SVMP6TFTN6PFCRHX":3,"skill-same:01M6AEMJR7SVMP6TFTN6PFCRHX":125},{"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":41,"linkOnly":42,"markdownContent":43,"markdownBody":44,"frontMatter":45,"refName":66,"commitSha":67,"skillDirectory":68,"forksCount":69,"repositoryHtmlUrl":70,"repositoryHomepageUrl":71,"ownerHtmlUrl":72,"ownerBlogUrl":73,"ownerDescription":74,"updatedAt":75,"install":76,"resources":89},"01M6AEMJR7SVMP6TFTN6PFCRHX","exploiting-active-directory-with-bloodhound",null,"BloodHound is a graph-based Active Directory reconnaissance tool that uses graph theory to reveal hidden and unintended relationships within AD environments. Red teams use BloodHound to identify attac","mukul975\u002FAnthropic-Cybersecurity-Skills","mukul975","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F42860185?v=4","01M6ADEQR8T5M4EEG6C67BQCWF","Mahipal","01M6ADJPMYWG1E5GM1FWBE3128",35925,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fexploiting-active-directory-with-bloodhound\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40],"exploiting active directory with bloodhound","bloodhound","directory","active-directory","t1059","t1078","exploitation","reconnaissance","adversary-simulation","mitre-attack","post-exploitation","red-team","graph","authentication","teams","cybersecurity","policy","2026-11-10 10:12:30",false,"---\nname: exploiting-active-directory-with-bloodhound\ndescription: BloodHound is a graph-based Active Directory reconnaissance tool that\n  uses graph theory to reveal hidden and unintended relationships within AD environments.\n  Red teams use BloodHound to identify attac\ndomain: cybersecurity\nsubdomain: red-teaming\ntags:\n- red-team\n- adversary-simulation\n- mitre-attack\n- exploitation\n- post-exploitation\n- active-directory\n- bloodhound\nversion: '1.0'\nauthor: mahipal\nlicense: Apache-2.0\nd3fend_techniques:\n- Restore Access\n- Password Authentication\n- Biometric Authentication\n- Strong Password Policy\n- Restore User Account Access\nnist_csf:\n- ID.RA-01\n- GV.OV-02\n- DE.AE-07\nmitre_attack:\n- T1595\n- T1190\n- T1059\n- T1078\n- T1592\n---\n# Exploiting Active Directory with BloodHound\n\n\n> **Legal Notice:** This skill is for authorized security testing and educational purposes only. Unauthorized use against systems you do not own or have written permission to test is illegal and may violate computer fraud laws.\n\n## Overview\n\nBloodHound is a graph-based Active Directory reconnaissance tool that uses graph theory to reveal hidden and unintended relationships within AD environments. Red teams use BloodHound to identify attack paths from compromised accounts to high-value targets such as Domain Admins, identifying privilege escalation chains that would be nearly impossible to find manually. SharpHound is the official data collector that gathers AD objects, relationships, ACLs, sessions, and group memberships.\n\n\n## When to Use\n\n- When performing authorized security testing that involves exploiting active directory with bloodhound\n- When analyzing malware samples or attack artifacts in a controlled environment\n- When conducting red team exercises or penetration testing engagements\n- When building detection capabilities based on offensive technique understanding\n\n## Prerequisites\n\n- Familiarity with red teaming concepts and tools\n- Access to a test or lab environment for safe execution\n- Python 3.8+ with required dependencies installed\n- Appropriate authorization for any testing activities\n\n## Objectives\n\n- Collect Active Directory relationship data using SharpHound or BloodHound.py\n- Visualize attack paths from compromised accounts to Domain Admin\n- Identify misconfigured ACLs, group memberships, and delegation settings\n- Discover shortest attack paths to high-value targets\n- Map Kerberos delegation configurations for abuse\n- Document all identified privilege escalation chains\n\n## MITRE ATT&CK Mapping\n\n- **T1087.002** - Account Discovery: Domain Account\n- **T1069.002** - Permission Groups Discovery: Domain Groups\n- **T1482** - Domain Trust Discovery\n- **T1615** - Group Policy Discovery\n- **T1018** - Remote System Discovery\n- **T1033** - System Owner\u002FUser Discovery\n- **T1016** - System Network Configuration Discovery\n\n## Workflow\n\n### Phase 1: Data Collection with SharpHound\n1. Transfer SharpHound collector to compromised host\n2. Execute collection with appropriate method (All, DCOnly, Session, LoggedOn)\n3. Collect from all reachable domains if multi-domain environment\n4. Exfiltrate ZIP data files to analysis workstation\n5. Import data into BloodHound CE or Legacy\n\n### Phase 2: Attack Path Analysis\n1. Mark owned principals (compromised accounts)\n2. Query shortest path to Domain Admins\n3. Identify Kerberoastable accounts with admin privileges\n4. Find AS-REP Roastable accounts\n5. Analyze ACL-based attack paths (GenericAll, GenericWrite, WriteDACL, ForceChangePassword)\n6. Review GPO abuse opportunities\n\n### Phase 3: Exploitation Planning\n1. Prioritize attack paths by complexity and stealth\n2. Identify required tools for each step in the chain\n3. Plan OPSEC considerations for each technique\n4. Execute identified attack chain\n5. Document evidence at each step\n\n## Tools and Resources\n\n| Tool | Purpose | Platform |\n|------|---------|----------|\n| BloodHound CE | Graph visualization and analysis | Web-based |\n| SharpHound | AD data collection (.NET) | Windows |\n| BloodHound.py | AD data collection (Python) | Linux\u002FWindows |\n| Cypher queries | Custom graph queries | Neo4j\u002FBloodHound |\n| PlumHound | Automated BloodHound reporting | Python |\n| Max (BloodHound) | BloodHound automation | Python |\n\n## Key BloodHound Queries\n\n| Query | Purpose |\n|-------|---------|\n| Shortest Path to Domain Admins | Find fastest route to DA |\n| Find Kerberoastable Users with Path to DA | SPN accounts leading to DA |\n| Find AS-REP Roastable Users | Accounts without pre-auth |\n| Shortest Path from Owned Principals | Paths from compromised accounts |\n| Find Computers with Unsupported OS | Legacy systems for exploitation |\n| Find Users with DCSync Rights | Accounts that can replicate AD |\n| Find GPOs that Modify Local Group Membership | GPO-based privilege escalation |\n\n## Validation Criteria\n\n- [ ] SharpHound data collected from all domains\n- [ ] Attack paths identified from owned accounts to DA\n- [ ] ACL-based attack paths documented\n- [ ] Kerberoastable and AS-REP roastable accounts identified\n- [ ] Exploitation plan created with prioritized paths\n- [ ] Evidence screenshots captured for report\n","# Exploiting Active Directory with BloodHound\n\n\n> **Legal Notice:** This skill is for authorized security testing and educational purposes only. Unauthorized use against systems you do not own or have written permission to test is illegal and may violate computer fraud laws.\n\n## Overview\n\nBloodHound is a graph-based Active Directory reconnaissance tool that uses graph theory to reveal hidden and unintended relationships within AD environments. Red teams use BloodHound to identify attack paths from compromised accounts to high-value targets such as Domain Admins, identifying privilege escalation chains that would be nearly impossible to find manually. SharpHound is the official data collector that gathers AD objects, relationships, ACLs, sessions, and group memberships.\n\n\n## When to Use\n\n- When performing authorized security testing that involves exploiting active directory with bloodhound\n- When analyzing malware samples or attack artifacts in a controlled environment\n- When conducting red team exercises or penetration testing engagements\n- When building detection capabilities based on offensive technique understanding\n\n## Prerequisites\n\n- Familiarity with red teaming concepts and tools\n- Access to a test or lab environment for safe execution\n- Python 3.8+ with required dependencies installed\n- Appropriate authorization for any testing activities\n\n## Objectives\n\n- Collect Active Directory relationship data using SharpHound or BloodHound.py\n- Visualize attack paths from compromised accounts to Domain Admin\n- Identify misconfigured ACLs, group memberships, and delegation settings\n- Discover shortest attack paths to high-value targets\n- Map Kerberos delegation configurations for abuse\n- Document all identified privilege escalation chains\n\n## MITRE ATT&CK Mapping\n\n- **T1087.002** - Account Discovery: Domain Account\n- **T1069.002** - Permission Groups Discovery: Domain Groups\n- **T1482** - Domain Trust Discovery\n- **T1615** - Group Policy Discovery\n- **T1018** - Remote System Discovery\n- **T1033** - System Owner\u002FUser Discovery\n- **T1016** - System Network Configuration Discovery\n\n## Workflow\n\n### Phase 1: Data Collection with SharpHound\n1. Transfer SharpHound collector to compromised host\n2. Execute collection with appropriate method (All, DCOnly, Session, LoggedOn)\n3. Collect from all reachable domains if multi-domain environment\n4. Exfiltrate ZIP data files to analysis workstation\n5. Import data into BloodHound CE or Legacy\n\n### Phase 2: Attack Path Analysis\n1. Mark owned principals (compromised accounts)\n2. Query shortest path to Domain Admins\n3. Identify Kerberoastable accounts with admin privileges\n4. Find AS-REP Roastable accounts\n5. Analyze ACL-based attack paths (GenericAll, GenericWrite, WriteDACL, ForceChangePassword)\n6. Review GPO abuse opportunities\n\n### Phase 3: Exploitation Planning\n1. Prioritize attack paths by complexity and stealth\n2. Identify required tools for each step in the chain\n3. Plan OPSEC considerations for each technique\n4. Execute identified attack chain\n5. Document evidence at each step\n\n## Tools and Resources\n\n| Tool | Purpose | Platform |\n|------|---------|----------|\n| BloodHound CE | Graph visualization and analysis | Web-based |\n| SharpHound | AD data collection (.NET) | Windows |\n| BloodHound.py | AD data collection (Python) | Linux\u002FWindows |\n| Cypher queries | Custom graph queries | Neo4j\u002FBloodHound |\n| PlumHound | Automated BloodHound reporting | Python |\n| Max (BloodHound) | BloodHound automation | Python |\n\n## Key BloodHound Queries\n\n| Query | Purpose |\n|-------|---------|\n| Shortest Path to Domain Admins | Find fastest route to DA |\n| Find Kerberoastable Users with Path to DA | SPN accounts leading to DA |\n| Find AS-REP Roastable Users | Accounts without pre-auth |\n| Shortest Path from Owned Principals | Paths from compromised accounts |\n| Find Computers with Unsupported OS | Legacy systems for exploitation |\n| Find Users with DCSync Rights | Accounts that can replicate AD |\n| Find GPOs that Modify Local Group Membership | GPO-based privilege escalation |\n\n## Validation Criteria\n\n- [ ] SharpHound data collected from all domains\n- [ ] Attack paths identified from owned accounts to DA\n- [ ] ACL-based attack paths documented\n- [ ] Kerberoastable and AS-REP roastable accounts identified\n- [ ] Exploitation plan created with prioritized paths\n- [ ] Evidence screenshots captured for report\n",{"name":5,"tags":46,"author":47,"domain":39,"license":15,"version":48,"nist_csf":49,"subdomain":53,"description":7,"mitre_attack":54,"d3fend_techniques":60},[35,32,33,30,34,27,25],"mahipal","1.0",[50,51,52],"ID.RA-01","GV.OV-02","DE.AE-07","red-teaming",[55,56,57,58,59],"T1595","T1190","T1059","T1078","T1592",[61,62,63,64,65],"Restore Access","Password Authentication","Biometric Authentication","Strong Password Policy","Restore User Account Access","main","54a798831d2266a3ca61ce68a7acb80b81160d57","skills\u002Fexploiting-active-directory-with-bloodhound",4384,"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":77,"prompt":87,"zipAvailable":88},[78,81,84],{"tool":79,"command":80},"npx","npx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill exploiting-active-directory-with-bloodhound",{"tool":82,"command":83},"bunx","bunx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill exploiting-active-directory-with-bloodhound",{"tool":85,"command":86},"pnpm","pnpm dlx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill 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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",[132],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[134,135,136,137,138,39,139,140,141,142,143,144,145,146,147,148,149,150,151,152],"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":154,"name":155,"nameZh":6,"description":156,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":157,"categories":158,"keywords":160,"publishedAt":41},"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",[159],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[161,149,162,163,138,40,39,141,164,165,166,167,168,169,170,171,172,173,174,175],"boot","TPM","validating tpm measured boot attestation","verification","ZeRO","firmware","Tools","measured-boot","PCR","integrity","secrets","remote-attestation","tpm2-tools","integrity-verification","trusted-computing",{"skillNo":177,"name":178,"nameZh":6,"description":179,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":180,"categories":181,"keywords":186,"publishedAt":41},"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",[182,185],{"categoryNo":183,"categoryName":184,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[170,187,188,189,190,39,191,192,164,193,194,195,196,197,198,29,199,200,201,202],"backup","recovery","validating backup integrity for recovery","testing","infrastructure","validation","compliance","hash-verification","incident-response","ransomware","cryptocurrency","detection","t1490","immutable","disaster-recovery","restore-testing",{"skillNo":204,"name":205,"nameZh":6,"description":206,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":207,"categories":208,"keywords":210,"publishedAt":41},"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",[209],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[211,212,213,39,214,215,216,217,218,219,220,221,222,223,224,225,167,226,227,228],"windows","kape","triaging windows with kape","registry","browser","logs","windows-forensics","dfir","mft","mftecmd","data","prefetch","eric-zimmerman","digital-forensics","SET","collection","pecmd","artifact-collection",{"skillNo":230,"name":231,"nameZh":6,"description":232,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":233,"categories":234,"keywords":236,"publishedAt":41},"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",[235],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[237,238,239,39,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255],"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":257,"name":258,"nameZh":6,"description":259,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":260,"categories":261,"keywords":263,"publishedAt":41},"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",[262],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[138,264,39,265,266,267,268,269,270,271,272,29,273,199,274,275,276,277,278,279],"triaging security incident","siem","file","ioc","soc","NIST-800-61","NIST","severity","edr","execution","analysis","classification","severity-classification","incident-triage","SANS-PICERL","SOC-operations"]