[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6AEGQXYRR48M7XH9ECVC0DT":3,"skill-same:01M6AEGQXYRR48M7XH9ECVC0DT":167},{"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":34,"publishedAt":55,"linkOnly":56,"markdownContent":57,"markdownBody":58,"frontMatter":59,"refName":108,"commitSha":109,"skillDirectory":110,"forksCount":111,"repositoryHtmlUrl":112,"repositoryHomepageUrl":113,"ownerHtmlUrl":114,"ownerBlogUrl":115,"ownerDescription":116,"updatedAt":117,"install":118,"resources":131},"01M6AEGQXYRR48M7XH9ECVC0DT","detecting-qr-code-phishing-with-email-security",null,"Detect and prevent QR code phishing (quishing) attacks that embed malicious URLs inside QR code images to bypass link-based email security, using image-based threat detection, OCR\u002FQR decoding, and mobile-side scanning (Microsoft Defender for O365, Proofpoint TAP, Barracuda Multimodal AI). Use when configuring gateway rules against QR phishing or investigating suspicious emails containing QR codes.","mukul975\u002FAnthropic-Cybersecurity-Skills","mukul975","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F42860185?v=4","01M6ADEQR8T5M4EEG6C67BQCWF","Mahipal","01M6ADJPMYWG1E5GM1FWBE3128",35990,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fdetecting-qr-code-phishing-with-email-security\u002FSKILL.md",[18,23,26,29],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000005","邮件与沟通","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":30,"categoryName":31,"taxonomyType":32,"taxonomyTypeDesc":33},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54],"detecting qr code phishing with email security","security","phishing","email","code","multimodal","ocr","qr-code","defense","image-analysis","gateway","website","image","create","infrastructure","pr","defender","cybersecurity","suspicious","malicious","2026-11-10 10:12:30",false,"---\nname: detecting-qr-code-phishing-with-email-security\ndescription: Detect and prevent QR code phishing (quishing) attacks that embed\n  malicious URLs inside QR code images to bypass link-based email security,\n  using image-based threat detection, OCR\u002FQR decoding, and mobile-side scanning\n  (Microsoft Defender for O365, Proofpoint TAP, Barracuda Multimodal AI). Use\n  when configuring gateway rules against QR phishing or investigating suspicious\n  emails containing QR codes.\ndomain: cybersecurity\nsubdomain: phishing-defense\ntags:\n- quishing\n- qr-code\n- phishing\n- email-security\n- image-analysis\n- ocr\n- mobile-security\nversion: '1.0'\nauthor: mahipal\nlicense: Apache-2.0\natlas_techniques:\n- AML.T0052\n- AML.T0024\n- AML.T0035\nnist_ai_rmf:\n- MEASURE-2.8\n- MAP-5.1\nnist_csf:\n- PR.AT-01\n- DE.CM-09\n- RS.CO-02\n- DE.AE-02\nmitre_attack:\n- T1566\n- T1598\n- T1534\n- T1036\n- T1027\nmitre_f3:\n  version: '1.1'\n  tactics:\n  - reconnaissance\n  - resource-development\n  - initial-access\n  techniques:\n  - id: T1598\n    name: Phishing for Information\n    tactic: reconnaissance\n    source: attack\n  - id: T1660\n    name: Phishing\n    tactic: initial-access\n    source: attack\n  - id: F1020.002\n    name: 'Create Fake Materials: Fake Website'\n    tactic: resource-development\n    source: f3\n  - id: T1583.001\n    name: 'Acquire Infrastructure: Domains'\n    tactic: resource-development\n    source: attack\n  - id: F1006.002\n    name: 'Account Takeover: Exposed Login Credential'\n    tactic: initial-access\n    source: f3\n---\n# Detecting QR Code Phishing with Email Security\n\n## Overview\nQR code phishing (quishing) is a rapidly growing attack vector where malicious URLs are embedded in QR code images within phishing emails. Quishing incidents grew fivefold from 46,000 to 250,000 between August and November 2025, with credential phishing comprising 89.3% of detected incidents. Traditional email security filters struggle because QR codes cannot be read by humans or standard URL scanners, and when scanned, users typically use personal mobile devices that lack corporate security controls. Attackers have evolved to use split QR codes (two separate images), nested QR codes, and ASCII text-based QR codes to evade detection.\n\n\n## When to Use\n\n- When investigating security incidents that require detecting qr code phishing with email security\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- Email security gateway with image analysis capabilities\n- Understanding of QR code structure and encoding\n- Mobile device management (MDM) or mobile threat defense solution\n- Security awareness training program\n- SIEM platform for correlation and alerting\n\n## Key Concepts\n\n### Why Quishing Works\n1. **Bypasses URL Scanners**: Traditional gateways scan text-based URLs but cannot decode image-embedded URLs\n2. **Shifts to Unprotected Devices**: Corporate email arrives on secured systems but QR scan occurs on personal mobile devices\n3. **User Trust**: QR codes are normalized in daily life (payments, menus, parking)\n4. **Low Detection Rate**: Only 36% of quishing incidents are accurately identified by recipients\n\n### Evasion Techniques (2025)\n- **Split QR Codes**: QR code divided into two separate images that look benign individually (Gabagool PhaaS kit)\n- **Nested QR Codes**: QR code within a QR code, with first scan leading to intermediate page\n- **ASCII QR Codes**: QR rendered as text characters instead of images, bypassing image analysis (12% of attacks in Jan 2026)\n- **Styled\u002FArtistic QR Codes**: Custom-designed QR codes with logos that evade pattern matching\n- **PDF Attachment QR**: QR code embedded in PDF attachment rather than email body\n\n### Detection Challenges\n- Pattern-based detection faces trade-off: aggressive tuning causes false positives, cautious tuning causes misses\n- Average similarity score of 0.209 between quishing and legitimate QR emails\n- QR codes in image attachments require OCR and deep image processing\n\n## Workflow\n\n### Step 1: Enable Image-Based Threat Detection\n- Configure email gateway to scan embedded images for QR codes\n- Enable OCR processing on image attachments (PNG, JPG, GIF, BMP)\n- Deploy multimodal AI that combines image processing, OCR, and NLP analysis\n- Configure PDF scanning to detect QR codes within attachments\n- Set up detection for ASCII\u002Ftext-based QR code rendering\n\n### Step 2: Configure QR Code URL Analysis\n- Extract URLs from detected QR codes and submit to URL reputation services\n- Apply same URL scanning policies to QR-extracted URLs as text-based URLs\n- Enable real-time sandbox analysis for QR-decoded destination pages\n- Configure time-of-click protection for QR-extracted URLs where possible\n- Block known phishing domains extracted from QR codes\n\n### Step 3: Deploy Mobile-Side Protection\n- Implement mobile threat defense (MTD) with QR code scanning capability\n- Deploy Palo Alto ALFA or equivalent safe-by-design QR scanning\n- Configure MDM policies to warn users before opening scanned URLs\n- Enable corporate VPN\u002Fsecure browser for QR-scanned destinations\n- Block known credential harvesting domains at the mobile proxy level\n\n### Step 4: Build Detection Rules\n- Alert on emails containing only an image and minimal text (common quishing pattern)\n- Flag emails with QR code images from external first-time senders\n- Detect urgency language combined with QR code presence\n- Alert on emails impersonating IT\u002Fsecurity team requesting QR scan for MFA setup\n- Monitor for common quishing themes: MFA reset, document signing, voicemail notification\n\n### Step 5: Train Users on Quishing Recognition\n- Update security awareness program to include QR code phishing scenarios\n- Conduct quishing simulation campaigns using controlled QR codes\n- Teach users to verify QR destination URLs before entering credentials\n- Establish reporting process for suspicious QR code emails\n- Distribute guidance on safe QR scanning practices\n\n## Tools & Resources\n- **Barracuda Multimodal AI**: OCR + deep image processing for QR detection\n- **Palo Alto ALFA**: Safe-by-design QR code scanning assessment\n- **Microsoft Defender for O365**: QR code detection in email images\n- **Proofpoint TAP**: Image-based threat analysis with QR decoding\n- **Lookout\u002FZimperium**: Mobile threat defense with QR scanning\n\n## Validation\n- QR code phishing emails detected in controlled testing\n- Split QR code and ASCII QR code evasion techniques caught\n- QR-extracted URLs submitted to sandbox analysis\n- Mobile devices alert on malicious QR destinations\n- User reporting rate for quishing simulations exceeds 50%\n- False positive rate for QR detection below 1%\n","# Detecting QR Code Phishing with Email Security\n\n## Overview\nQR code phishing (quishing) is a rapidly growing attack vector where malicious URLs are embedded in QR code images within phishing emails. Quishing incidents grew fivefold from 46,000 to 250,000 between August and November 2025, with credential phishing comprising 89.3% of detected incidents. Traditional email security filters struggle because QR codes cannot be read by humans or standard URL scanners, and when scanned, users typically use personal mobile devices that lack corporate security controls. Attackers have evolved to use split QR codes (two separate images), nested QR codes, and ASCII text-based QR codes to evade detection.\n\n\n## When to Use\n\n- When investigating security incidents that require detecting qr code phishing with email security\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- Email security gateway with image analysis capabilities\n- Understanding of QR code structure and encoding\n- Mobile device management (MDM) or mobile threat defense solution\n- Security awareness training program\n- SIEM platform for correlation and alerting\n\n## Key Concepts\n\n### Why Quishing Works\n1. **Bypasses URL Scanners**: Traditional gateways scan text-based URLs but cannot decode image-embedded URLs\n2. **Shifts to Unprotected Devices**: Corporate email arrives on secured systems but QR scan occurs on personal mobile devices\n3. **User Trust**: QR codes are normalized in daily life (payments, menus, parking)\n4. **Low Detection Rate**: Only 36% of quishing incidents are accurately identified by recipients\n\n### Evasion Techniques (2025)\n- **Split QR Codes**: QR code divided into two separate images that look benign individually (Gabagool PhaaS kit)\n- **Nested QR Codes**: QR code within a QR code, with first scan leading to intermediate page\n- **ASCII QR Codes**: QR rendered as text characters instead of images, bypassing image analysis (12% of attacks in Jan 2026)\n- **Styled\u002FArtistic QR Codes**: Custom-designed QR codes with logos that evade pattern matching\n- **PDF Attachment QR**: QR code embedded in PDF attachment rather than email body\n\n### Detection Challenges\n- Pattern-based detection faces trade-off: aggressive tuning causes false positives, cautious tuning causes misses\n- Average similarity score of 0.209 between quishing and legitimate QR emails\n- QR codes in image attachments require OCR and deep image processing\n\n## Workflow\n\n### Step 1: Enable Image-Based Threat Detection\n- Configure email gateway to scan embedded images for QR codes\n- Enable OCR processing on image attachments (PNG, JPG, GIF, BMP)\n- Deploy multimodal AI that combines image processing, OCR, and NLP analysis\n- Configure PDF scanning to detect QR codes within attachments\n- Set up detection for ASCII\u002Ftext-based QR code rendering\n\n### Step 2: Configure QR Code URL Analysis\n- Extract URLs from detected QR codes and submit to URL reputation services\n- Apply same URL scanning policies to QR-extracted URLs as text-based URLs\n- Enable real-time sandbox analysis for QR-decoded destination pages\n- Configure time-of-click protection for QR-extracted URLs where possible\n- Block known phishing domains extracted from QR codes\n\n### Step 3: Deploy Mobile-Side Protection\n- Implement mobile threat defense (MTD) with QR code scanning capability\n- Deploy Palo Alto ALFA or equivalent safe-by-design QR scanning\n- Configure MDM policies to warn users before opening scanned URLs\n- Enable corporate VPN\u002Fsecure browser for QR-scanned destinations\n- Block known credential harvesting domains at the mobile proxy level\n\n### Step 4: Build Detection Rules\n- Alert on emails containing only an image and minimal text (common quishing pattern)\n- Flag emails with QR code images from external first-time senders\n- Detect urgency language combined with QR code presence\n- Alert on emails impersonating IT\u002Fsecurity team requesting QR scan for MFA setup\n- Monitor for common quishing themes: MFA reset, document signing, voicemail notification\n\n### Step 5: Train Users on Quishing Recognition\n- Update security awareness program to include QR code phishing scenarios\n- Conduct quishing simulation campaigns using controlled QR codes\n- Teach users to verify QR destination URLs before entering credentials\n- Establish reporting process for suspicious QR code emails\n- Distribute guidance on safe QR scanning practices\n\n## Tools & Resources\n- **Barracuda Multimodal AI**: OCR + deep image processing for QR detection\n- **Palo Alto ALFA**: Safe-by-design QR code scanning assessment\n- **Microsoft Defender for O365**: QR code detection in email images\n- **Proofpoint TAP**: Image-based threat analysis with QR decoding\n- **Lookout\u002FZimperium**: Mobile threat defense with QR scanning\n\n## Validation\n- QR code phishing emails detected in controlled testing\n- Split QR code and ASCII QR code evasion techniques caught\n- QR-extracted URLs submitted to sandbox analysis\n- Mobile devices alert on malicious QR destinations\n- User reporting rate for quishing simulations exceeds 50%\n- False positive rate for QR detection below 1%\n",{"name":5,"tags":60,"author":64,"domain":52,"license":15,"version":65,"mitre_f3":66,"nist_csf":90,"subdomain":95,"description":7,"nist_ai_rmf":96,"mitre_attack":99,"atlas_techniques":104},[61,42,37,62,44,41,63],"quishing","email-security","mobile-security","mahipal","1.0",{"tactics":67,"version":71,"techniques":72},[68,69,70],"reconnaissance","resource-development","initial-access","1.1",[73,77,80,84,87],{"id":74,"name":75,"source":76,"tactic":68},"T1598","Phishing for Information","attack",{"id":78,"name":79,"source":76,"tactic":70},"T1660","Phishing",{"id":81,"name":82,"source":83,"tactic":69},"F1020.002","Create Fake Materials: Fake Website","f3",{"id":85,"name":86,"source":76,"tactic":69},"T1583.001","Acquire Infrastructure: Domains",{"id":88,"name":89,"source":83,"tactic":70},"F1006.002","Account Takeover: Exposed Login 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detecting-qr-code-phishing-with-email-security","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fdetecting-qr-code-phishing-with-email-security\u002FSKILL.md",true,[132,138,143,149,153,157,163],{"path":133,"resourceType":134,"resourceTypeDesc":135,"sizeBytes":136,"sourceHtmlUrl":137},"assets\u002Ftemplate.md","ASSET","资产文件",1502,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fdetecting-qr-code-phishing-with-email-security\u002Fassets\u002Ftemplate.md",{"path":139,"resourceType":139,"resourceTypeDesc":140,"sizeBytes":141,"sourceHtmlUrl":142},"LICENSE","许可证",11255,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fdetecting-qr-code-phishing-with-email-security\u002FLICENSE",{"path":144,"resourceType":145,"resourceTypeDesc":146,"sizeBytes":147,"sourceHtmlUrl":148},"references\u002Fapi-reference.md","REFERENCE","参考文档",2445,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fdetecting-qr-code-phishing-with-email-security\u002Freferences\u002Fapi-reference.md",{"path":150,"resourceType":145,"resourceTypeDesc":146,"sizeBytes":151,"sourceHtmlUrl":152},"references\u002Fstandards.md",1668,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fdetecting-qr-code-phishing-with-email-security\u002Freferences\u002Fstandards.md",{"path":154,"resourceType":145,"resourceTypeDesc":146,"sizeBytes":155,"sourceHtmlUrl":156},"references\u002Fworkflows.md",2558,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fdetecting-qr-code-phishing-with-email-security\u002Freferences\u002Fworkflows.md",{"path":158,"resourceType":159,"resourceTypeDesc":160,"sizeBytes":161,"sourceHtmlUrl":162},"scripts\u002Fagent.py","SCRIPT","脚本",6055,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fdetecting-qr-code-phishing-with-email-security\u002Fscripts\u002Fagent.py",{"path":164,"resourceType":159,"resourceTypeDesc":160,"sizeBytes":165,"sourceHtmlUrl":166},"scripts\u002Fprocess.py",11736,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fdetecting-qr-code-phishing-with-email-security\u002Fscripts\u002Fprocess.py",[168,192,216,243,269,296],{"skillNo":169,"name":170,"nameZh":6,"description":171,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":172,"categories":173,"keywords":175,"publishedAt":55},"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",[174],{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},[176,177,178,179,36,52,180,181,50,39,182,183,184,185,186,187,188,189,190,191],"sigstore","provenance","slsa","verifying build provenance with slsa sigstore","incident response","identity","artifacts","ci","supply-chain","pipeline","supply-chain-security","t1195","attestation","code-signing","cosign","keyless",{"skillNo":193,"name":194,"nameZh":6,"description":195,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":196,"categories":197,"keywords":199,"publishedAt":55},"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",[198],{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},[200,188,201,202,36,203,52,50,204,205,206,207,208,209,210,211,212,213,214,215],"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":217,"name":218,"nameZh":6,"description":219,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":220,"categories":221,"keywords":226,"publishedAt":55},"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",[222,225],{"categoryNo":223,"categoryName":224,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},[210,227,228,229,230,52,49,231,204,232,233,234,235,236,237,238,239,240,241,242],"backup","recovery","validating backup integrity for recovery","testing","validation","compliance","hash-verification","incident-response","ransomware","cryptocurrency","detection","t1078","t1490","immutable","disaster-recovery","restore-testing",{"skillNo":244,"name":245,"nameZh":6,"description":246,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":247,"categories":248,"keywords":250,"publishedAt":55},"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",[249],{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},[251,252,253,52,254,255,256,257,258,259,260,261,262,263,264,265,207,266,267,268],"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":270,"name":271,"nameZh":6,"description":272,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":273,"categories":274,"keywords":276,"publishedAt":55},"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",[275],{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},[277,278,279,52,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295],"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":297,"name":298,"nameZh":6,"description":299,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":300,"categories":301,"keywords":303,"publishedAt":55},"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",[302],{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},[36,304,52,305,306,307,308,309,310,311,312,238,313,239,314,315,316,317,318,319],"triaging security incident","siem","file","ioc","soc","NIST-800-61","NIST","severity","edr","execution","analysis","classification","severity-classification","incident-triage","SANS-PICERL","SOC-operations"]