[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6ADMHGDKK0HG5XVD3FK7T6P":3,"skill-same:01M6ADMHGDKK0HG5XVD3FK7T6P":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":18,"publishedAt":37,"linkOnly":38,"markdownContent":39,"markdownBody":40,"frontMatter":41,"refName":56,"commitSha":57,"skillDirectory":58,"forksCount":59,"repositoryHtmlUrl":60,"repositoryHomepageUrl":61,"ownerHtmlUrl":62,"ownerBlogUrl":63,"ownerDescription":64,"updatedAt":65,"install":66,"resources":79},"01M6ADMHGDKK0HG5XVD3FK7T6P","analyzing-cobaltstrike-malleable-c2-profiles",null,"Parse and analyze Cobalt Strike Malleable C2 profiles with dissect.cobaltstrike (profiles and beacon-payload configs) and pyMalleableC2 (AST parsing) to extract HTTP\u002FDNS transforms, URIs, headers, sleep\u002Fjitter, and injection behavior, then generate network detection signatures. Use when reverse-engineering a captured malleable profile or building detections against Cobalt Strike Beacon traffic.","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\u002Fanalyzing-cobaltstrike-malleable-c2-profiles\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36],"analyzing cobaltstrike malleable c2 profiles","c2","beacon","network-signatures","beacon-analysis","c2-detection","malleable-c2","red-team-tools","malware-analysis","cybersecurity","cobalt-strike","malware","threat-hunting","reverse-engineering","red-team","engineering","generate","http","2026-11-10 10:12:30",false,"---\nname: analyzing-cobaltstrike-malleable-c2-profiles\ndescription: Parse and analyze Cobalt Strike Malleable C2 profiles with dissect.cobaltstrike (profiles and beacon-payload configs) and pyMalleableC2 (AST parsing) to extract HTTP\u002FDNS transforms, URIs, headers, sleep\u002Fjitter, and injection behavior, then generate network detection signatures. Use when reverse-engineering a captured malleable profile or building detections against Cobalt Strike Beacon traffic.\ndomain: cybersecurity\nsubdomain: malware-analysis\ntags:\n- cobalt-strike\n- malleable-c2\n- c2-detection\n- beacon-analysis\n- network-signatures\n- threat-hunting\n- red-team-tools\nversion: '1.0'\nauthor: mahipal\nlicense: Apache-2.0\nnist_csf:\n- DE.AE-02\n- RS.AN-03\n- ID.RA-01\n- DE.CM-01\nmitre_attack:\n- T1071.001\n- T1573.002\n- T1001.003\n- T1090.004\n- T1102\n---\n# Analyzing CobaltStrike Malleable C2 Profiles\n\n## Overview\n\nCobalt Strike Malleable C2 profiles are domain-specific language scripts that customize how Beacon communicates with the team server, defining HTTP request\u002Fresponse transformations, sleep intervals, jitter values, user agents, URI paths, and process injection behavior. Threat actors use malleable profiles to disguise C2 traffic as legitimate services (Amazon, Google, Slack). Analyzing these profiles reveals network indicators for detection: URI patterns, HTTP headers, POST\u002FGET transforms, DNS settings, and process injection techniques. The `dissect.cobaltstrike` library can parse both profile files and extract configurations from beacon payloads, while `pyMalleableC2` provides AST-based parsing using Lark grammar for programmatic profile manipulation and validation.\n\n\n## When to Use\n\n- When investigating security incidents that require analyzing cobaltstrike malleable c2 profiles\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- Python 3.9+ with `dissect.cobaltstrike` and\u002For `pyMalleableC2`\n- Sample Malleable C2 profiles (available from public repositories)\n- Understanding of HTTP protocol and Cobalt Strike beacon communication model\n- Network monitoring tools (Suricata\u002FSnort) for signature deployment\n- PCAP analysis tools for traffic validation\n\n## Steps\n\n1. Install libraries: `pip install dissect.cobaltstrike` or `pip install pyMalleableC2`\n2. Parse profile with `C2Profile.from_path(\"profile.profile\")`\n3. Extract HTTP GET\u002FPOST block configurations (URIs, headers, parameters)\n4. Identify user agent strings and spoof targets\n5. Extract sleep time, jitter percentage, and DNS beacon settings\n6. Analyze process injection settings (spawn-to, allocation technique)\n7. Generate Suricata\u002FSnort signatures from extracted network indicators\n8. Compare profile against known threat actor profile collections\n9. Extract staging URIs and payload delivery mechanisms\n10. Produce detection report with IOCs and recommended network signatures\n\n## Expected Output\n\nA JSON report containing extracted C2 URIs, HTTP headers, user agents, sleep\u002Fjitter settings, process injection config, spawned process paths, DNS settings, and generated Suricata-compatible detection rules.\n","# Analyzing CobaltStrike Malleable C2 Profiles\n\n## Overview\n\nCobalt Strike Malleable C2 profiles are domain-specific language scripts that customize how Beacon communicates with the team server, defining HTTP request\u002Fresponse transformations, sleep intervals, jitter values, user agents, URI paths, and process injection behavior. Threat actors use malleable profiles to disguise C2 traffic as legitimate services (Amazon, Google, Slack). Analyzing these profiles reveals network indicators for detection: URI patterns, HTTP headers, POST\u002FGET transforms, DNS settings, and process injection techniques. The `dissect.cobaltstrike` library can parse both profile files and extract configurations from beacon payloads, while `pyMalleableC2` provides AST-based parsing using Lark grammar for programmatic profile manipulation and validation.\n\n\n## When to Use\n\n- When investigating security incidents that require analyzing cobaltstrike malleable c2 profiles\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- Python 3.9+ with `dissect.cobaltstrike` and\u002For `pyMalleableC2`\n- Sample Malleable C2 profiles (available from public repositories)\n- Understanding of HTTP protocol and Cobalt Strike beacon communication model\n- Network monitoring tools (Suricata\u002FSnort) for signature deployment\n- PCAP analysis tools for traffic validation\n\n## Steps\n\n1. Install libraries: `pip install dissect.cobaltstrike` or `pip install pyMalleableC2`\n2. Parse profile with `C2Profile.from_path(\"profile.profile\")`\n3. Extract HTTP GET\u002FPOST block configurations (URIs, headers, parameters)\n4. Identify user agent strings and spoof targets\n5. Extract sleep time, jitter percentage, and DNS beacon settings\n6. Analyze process injection settings (spawn-to, allocation technique)\n7. Generate Suricata\u002FSnort signatures from extracted network indicators\n8. Compare profile against known threat actor profile collections\n9. Extract staging URIs and payload delivery mechanisms\n10. Produce detection report with IOCs and recommended network signatures\n\n## Expected Output\n\nA JSON report containing extracted C2 URIs, HTTP headers, user agents, sleep\u002Fjitter settings, process injection config, spawned process paths, DNS settings, and generated Suricata-compatible detection rules.\n",{"name":5,"tags":42,"author":43,"domain":28,"license":15,"version":44,"nist_csf":45,"subdomain":27,"description":7,"mitre_attack":50},[29,25,24,23,22,31,26],"mahipal","1.0",[46,47,48,49],"DE.AE-02","RS.AN-03","ID.RA-01","DE.CM-01",[51,52,53,54,55],"T1071.001","T1573.002","T1001.003","T1090.004","T1102","main","54a798831d2266a3ca61ce68a7acb80b81160d57","skills\u002Fanalyzing-cobaltstrike-malleable-c2-profiles",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":67,"prompt":77,"zipAvailable":78},[68,71,74],{"tool":69,"command":70},"npx","npx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill analyzing-cobaltstrike-malleable-c2-profiles",{"tool":72,"command":73},"bunx","bunx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill analyzing-cobaltstrike-malleable-c2-profiles",{"tool":75,"command":76},"pnpm","pnpm dlx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill analyzing-cobaltstrike-malleable-c2-profiles","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fanalyzing-cobaltstrike-malleable-c2-profiles\u002FSKILL.md",true,[80,85,89,95],{"path":81,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":83,"sourceHtmlUrl":84},"LICENSE","许可证",11255,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fanalyzing-cobaltstrike-malleable-c2-profiles\u002FLICENSE",{"path":86,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":87,"sourceHtmlUrl":88},"LICENSE.txt",1467,"https:\u002F\u002Fgithub.com\u002Fderseitenschneider\u002Fdotfiles\u002Fblob\u002Fmain\u002Fclaude-config\u002F.claude\u002Fskills\u002Fsynced\u002F0ce0b40f-f33c-4454-8a14-bd4a36250724_492d02ae-7d32-4227-a9d4-d9f601100de4\u002Fchrome-browser\u002FLICENSE.txt",{"path":90,"resourceType":91,"resourceTypeDesc":92,"sizeBytes":93,"sourceHtmlUrl":94},"references\u002Fapi-reference.md","REFERENCE","参考文档",2808,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fanalyzing-cobaltstrike-malleable-c2-profiles\u002Freferences\u002Fapi-reference.md",{"path":96,"resourceType":97,"resourceTypeDesc":98,"sizeBytes":99,"sourceHtmlUrl":100},"scripts\u002Fagent.py","SCRIPT","脚本",8507,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fanalyzing-cobaltstrike-malleable-c2-profiles\u002Fscripts\u002Fagent.py",[102,133,157,185,211,238],{"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":113,"publishedAt":37},"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":109,"categoryName":110,"taxonomyType":111,"taxonomyTypeDesc":112},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[114,115,116,117,118,28,119,120,121,122,123,124,125,126,127,128,129,130,131,132],"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":134,"name":135,"nameZh":6,"description":136,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":137,"categories":138,"keywords":140,"publishedAt":37},"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",[139],{"categoryNo":109,"categoryName":110,"taxonomyType":111,"taxonomyTypeDesc":112},[141,129,142,143,118,144,28,121,145,146,147,148,149,150,151,152,153,154,155,156],"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":158,"name":159,"nameZh":6,"description":160,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":161,"categories":162,"keywords":167,"publishedAt":37},"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",[163,166],{"categoryNo":164,"categoryName":165,"taxonomyType":111,"taxonomyTypeDesc":112},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":109,"categoryName":110,"taxonomyType":111,"taxonomyTypeDesc":112},[151,168,169,170,171,28,172,173,145,174,175,176,177,178,179,180,181,182,183,184],"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":186,"name":187,"nameZh":6,"description":188,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":189,"categories":190,"keywords":192,"publishedAt":37},"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",[191],{"categoryNo":109,"categoryName":110,"taxonomyType":111,"taxonomyTypeDesc":112},[193,194,195,28,196,197,198,199,200,201,202,203,204,205,206,207,148,208,209,210],"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":212,"name":213,"nameZh":6,"description":214,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":215,"categories":216,"keywords":218,"publishedAt":37},"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",[217],{"categoryNo":109,"categoryName":110,"taxonomyType":111,"taxonomyTypeDesc":112},[219,220,221,28,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237],"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":239,"name":240,"nameZh":6,"description":241,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":242,"categories":243,"keywords":245,"publishedAt":37},"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",[244],{"categoryNo":109,"categoryName":110,"taxonomyType":111,"taxonomyTypeDesc":112},[118,246,28,247,248,249,250,251,252,253,254,180,255,181,256,257,258,259,260,261],"triaging security incident","siem","file","ioc","soc","NIST-800-61","NIST","severity","edr","execution","analysis","classification","severity-classification","incident-triage","SANS-PICERL","SOC-operations"]