[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71DKWQWF3G6E7GR5YHPQXC2":3,"skill-same:01M71DKWQWF3G6E7GR5YHPQXC2":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":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":63,"commitSha":64,"skillDirectory":65,"forksCount":14,"repositoryHtmlUrl":66,"repositoryHomepageUrl":6,"ownerHtmlUrl":67,"ownerBlogUrl":6,"ownerDescription":68,"updatedAt":69,"install":70,"resources":83},"01M71DKWQWF3G6E7GR5YHPQXC2","attack-credential-detect",null,"Detects credential stuffing attacks by analyzing authentication logs for login velocity anomalies, ASN diversity, password spray patterns, and geographic distribution of failed logins. Uses statistical analysis on Splunk or raw log data. Use when investigating account takeover campaigns or building detection rules for auth abuse.","DCx7C5\u002Fai-marketplace","DCx7C5","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F42419559?v=4","01M71C01G2AZV3NTNGWAWSHJPP","DC","01M71C01GQW6G50S2BYMS7PP3E",0,"MIT","https:\u002F\u002Fgithub.com\u002FDCx7C5\u002Fai-marketplace\u002Fblob\u002Fmain\u002Fskills\u002Fidentity\u002Fattack\u002Fcredential\u002Fdetect\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"attack credential detect","credential","detect","Bash","when","password","Rules","cybersecurity","use","t1059","diversity","authentication","takeover","cm","for","account takeover","auth","campaigns","distribution","attacks","2026-11-10 10:12:30",false,"---\nname: attack-credential-detect\ndescription: Detects credential stuffing attacks by analyzing authentication logs for login velocity anomalies, ASN diversity, password spray patterns, and geographic distribution of failed logins. Uses statistical analysis on Splunk or raw log data. Use when investigating account takeover campaigns or building detection rules for auth abuse.\ndomain: cybersecurity\nsubdomain: security-operations\ntags:\n- hunting\n- credential\n- stuffing\n- attacks\nnist_csf:\n- DE.CM-01\n- RS.MA-01\n- GV.OV-01\n- DE.AE-02\nmodel: sonnet\nmaxTurns: 20\ntools: [Read, Bash, Glob, Grep]\nmitre_attack:\n- T1003\n- T1059\ncapec: []\n---\n\n# Hunting Credential Stuffing Attacks\n\n\n## When to Use\n\n- When investigating security incidents that require hunting credential stuffing attacks\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- Familiarity with security operations 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## Instructions\n\nAnalyze authentication logs to detect credential stuffing by identifying patterns\nof distributed login failures, high IP diversity, and suspicious ASN distribution.\n\n```python\nimport pandas as pd\nfrom collections import Counter\n\n# Load auth logs\ndf = pd.read_csv(\"auth_logs.csv\", parse_dates=[\"timestamp\"])\n\n# Credential stuffing indicator: many IPs trying few accounts\nip_per_account = df[df[\"status\"] == \"failed\"].groupby(\"username\")[\"source_ip\"].nunique()\naccounts_under_attack = ip_per_account[ip_per_account > 50]\n```\n\nKey detection indicators:\n1. High unique source IPs per failed username\n2. Low success rate across many accounts (\u003C 1%)\n3. ASN concentration from cloud\u002Fproxy providers\n4. Geographic impossibility (same account, distant locations)\n5. User-agent uniformity across distributed IPs\n\n## Examples\n\n```python\n# Password spray: one password tried across many accounts\nspray = df[df[\"status\"] == \"failed\"].groupby([\"source_ip\", \"password_hash\"]).agg(\n    accounts=(\"username\", \"nunique\")).reset_index()\nsprays = spray[spray[\"accounts\"] > 10]\n```\n","# Hunting Credential Stuffing Attacks\n\n\n## When to Use\n\n- When investigating security incidents that require hunting credential stuffing attacks\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- Familiarity with security operations 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## Instructions\n\nAnalyze authentication logs to detect credential stuffing by identifying patterns\nof distributed login failures, high IP diversity, and suspicious ASN distribution.\n\n```python\nimport pandas as pd\nfrom collections import Counter\n\n# Load auth logs\ndf = pd.read_csv(\"auth_logs.csv\", parse_dates=[\"timestamp\"])\n\n# Credential stuffing indicator: many IPs trying few accounts\nip_per_account = df[df[\"status\"] == \"failed\"].groupby(\"username\")[\"source_ip\"].nunique()\naccounts_under_attack = ip_per_account[ip_per_account > 50]\n```\n\nKey detection indicators:\n1. High unique source IPs per failed username\n2. Low success rate across many accounts (\u003C 1%)\n3. ASN concentration from cloud\u002Fproxy providers\n4. Geographic impossibility (same account, distant locations)\n5. User-agent uniformity across distributed IPs\n\n## Examples\n\n```python\n# Password spray: one password tried across many accounts\nspray = df[df[\"status\"] == \"failed\"].groupby([\"source_ip\", \"password_hash\"]).agg(\n    accounts=(\"username\", \"nunique\")).reset_index()\nsprays = spray[spray[\"accounts\"] > 10]\n```\n",{"name":5,"tags":44,"capec":47,"model":48,"tools":49,"domain":26,"maxTurns":53,"nist_csf":54,"subdomain":59,"description":7,"mitre_attack":60},[45,20,46,38],"hunting","stuffing",[],"sonnet",[50,22,51,52],"Read","Glob","Grep",20,[55,56,57,58],"DE.CM-01","RS.MA-01","GV.OV-01","DE.AE-02","security-operations",[61,62],"T1003","T1059","main","a91f1293287ffa9f4f1b8f486cd6e835a1568c84","skills\u002Fidentity\u002Fattack\u002Fcredential\u002Fdetect","https:\u002F\u002Fgithub.com\u002FDCx7C5\u002Fai-marketplace","https:\u002F\u002Fgithub.com\u002FDCx7C5","#Python","2026-09-01 11:21:00",{"source":8,"skillName":5,"commands":71,"prompt":81,"zipAvailable":82},[72,75,78],{"tool":73,"command":74},"npx","npx skills add DCx7C5\u002Fai-marketplace --skill attack-credential-detect",{"tool":76,"command":77},"bunx","bunx skills add DCx7C5\u002Fai-marketplace --skill attack-credential-detect",{"tool":79,"command":80},"pnpm","pnpm dlx skills add DCx7C5\u002Fai-marketplace --skill attack-credential-detect","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FDCx7C5\u002Fai-marketplace\u002Fblob\u002Fmain\u002Fskills\u002Fidentity\u002Fattack\u002Fcredential\u002Fdetect\u002FSKILL.md",true,[84],{"path":85,"resourceType":86,"resourceTypeDesc":87,"sizeBytes":88,"sourceHtmlUrl":89},"index.json","REFERENCE","参考文档",689,"https:\u002F\u002Fgithub.com\u002FDCx7C5\u002Fai-marketplace\u002Fblob\u002Fmain\u002Fskills\u002Fidentity\u002Fattack\u002Fcredential\u002Fdetect\u002Findex.json",[91,114,136,157,178,194],{"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":97,"publishedAt":39},"01M71HAPZ01KQ30RKTB0FFSQ05","services-wmi-persistence-detect","Detect WMI event subscription persistence by enumerating ActiveScriptEventConsumer, CommandLineEventConsumer, and permanent subscriptions in the WMI repository.","https:\u002F\u002Fgithub.com\u002FDCx7C5\u002Fai-marketplace\u002Fblob\u002Fmain\u002Fskills\u002Fwindows\u002Fservices\u002Fwmi\u002Fpersistence\u002Fdetect\u002FSKILL.md",[],[98,21,99,100,101,102,32,103,104,105,106,107,108,109,110,111,112,26,113],"services","services wmi persistence detect","wmi","persistence","de","process","activescript","event","subscriptions","commandline-consumer","repository","subscription","event-subscription","t1546.003","forensics","process-forensics",{"skillNo":115,"name":116,"nameZh":6,"description":117,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":118,"categories":119,"keywords":120,"publishedAt":39},"01M71HAK6JTTTS229T17WJMMCG","services-wmi-exec-exploit","Perform lateral movement across Windows networks using WMI-based remote execution techniques including Impacket wmiexec.py, CrackMapExec, and native WMI commands for stealthy post-exploitation during red team engagements.","https:\u002F\u002Fgithub.com\u002FDCx7C5\u002Fai-marketplace\u002Fblob\u002Fmain\u002Fskills\u002Fwindows\u002Fservices\u002Fwmi\u002Fexec\u002Fexploit\u002FSKILL.md",[],[121,98,122,100,123,124,125,26,22,126,127,128,129,130,102,131,132,133,134,135],"exploit","services wmi exec exploit","exec","remote","traffic","analysis","team","across","crackmapexec","native","command","lateral movement","read","lateral-movement","Impacket",{"skillNo":137,"name":138,"nameZh":6,"description":139,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":140,"categories":141,"keywords":142,"publishedAt":39},"01M71HAG6MHKNQV8KDZKMT4886","registry-artifact-extract","Extract and analyze Windows Registry hives to uncover user activity, installed software, autostart entries, and evidence of system compromise.","https:\u002F\u002Fgithub.com\u002FDCx7C5\u002Fai-marketplace\u002Fblob\u002Fmain\u002Fskills\u002Fwindows\u002Fregistry\u002Fartifact\u002Fextract\u002FSKILL.md",[],[143,144,145,146,147,148,26,22,149,126,150,151,102,133,152,153,154,155,156,48],"artifact","registry","registry artifact extract","extract","evidence-collection","artifact-analysis","collection","analyze","uncover","software","activity","digital-forensics","windows-registry","regripper",{"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":163,"publishedAt":39},"01M71HACR4A8XTWTTSXK4124AA","processes-token-impersonate-detect","Detect Windows token impersonation and privilege abuse including SeImpersonatePrivilege exploitation via Potato attacks and token stealing techniques.","https:\u002F\u002Fgithub.com\u002FDCx7C5\u002Fai-marketplace\u002Fblob\u002Fmain\u002Fskills\u002Fwindows\u002Fprocesses\u002Ftoken\u002Fimpersonate\u002Fdetect\u002FSKILL.md",[],[21,164,165,166,38,167,168,32,169,26,113,170,171,172,173,174,175,176,177,102],"processes token impersonate detect","token","processes","windows","escalation","techniques","privilege-escalation","seimpersonate","token-impersonation","printspoofer","exploitation","potato","impersonation","via",{"skillNo":179,"name":180,"nameZh":6,"description":181,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":182,"categories":183,"keywords":184,"publishedAt":39},"01M71HAABSB4Z6QFSXEG00R4RK","processes-thread-injection-detect","Detect Windows thread injection techniques including CreateRemoteThread, QueueUserAPC, NtCreateThreadEx, and thread hijacking via process memory analysis.","https:\u002F\u002Fgithub.com\u002FDCx7C5\u002Fai-marketplace\u002Fblob\u002Fmain\u002Fskills\u002Fwindows\u002Fprocesses\u002Fthread\u002Finjection\u002Fdetect\u002FSKILL.md",[],[185,186,21,187,166,167,188,32,189,169,26,113,126,190,191,192,193,177,102,103],"thread","processes thread injection detect","injection","memory","process-injection","api","apc-injection","thread-injection","nt-api",{"skillNo":195,"name":196,"nameZh":6,"description":197,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":198,"categories":199,"keywords":200,"publishedAt":39},"01M71HA7H311V5G14WMX58HQSS","processes-hollow-technique-detect","Detect process hollowing (RunPE) by identifying processes with mismatched image paths between PEB and VAD tree, or unmapped PE headers in memory.","https:\u002F\u002Fgithub.com\u002FDCx7C5\u002Fai-marketplace\u002Fblob\u002Fmain\u002Fskills\u002Fwindows\u002Fprocesses\u002Fhollow\u002Ftechnique\u002Fdetect\u002FSKILL.md",[],[201,21,166,202,188,32,203,204,26,113,205,206,207,208,209,210,102,103,211,212],"processes hollow technique detect","tree","memory-forensics","process-hollow","peb","runpe","image","pe-header","in memory","with","mismatched","headers"]