[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6ADQK1N580CV3HNT2ZFSQCD":3,"skill-same:01M6ADQK1N580CV3HNT2ZFSQCD":127},{"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":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":68,"commitSha":69,"skillDirectory":70,"forksCount":71,"repositoryHtmlUrl":72,"repositoryHomepageUrl":73,"ownerHtmlUrl":74,"ownerBlogUrl":75,"ownerDescription":76,"updatedAt":77,"install":78,"resources":91},"01M6ADQK1N580CV3HNT2ZFSQCD","analyzing-network-covert-channels-in-malware",null,"Detect and analyze covert communication channels used by malware, including DNS tunneling, ICMP exfiltration, steganographic HTTP, and other protocol abuse used for C2 and data exfiltration. Use when investigating suspicious DNS\u002FICMP\u002FHTTP traffic patterns, hunting for hidden C2 channels in network captures, or attributing exfiltration traffic to a known tunneling toolset.","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\u002Fanalyzing-network-covert-channels-in-malware\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"analyzing network covert channels in malware","malware","malware-analysis","data-exfiltration","network-forensics","icmp-exfiltration","covert-channels","dns-tunneling","exfiltration","dns","c2-detection","suspicious","c2","forensics","file","communication","protocol","validation","http","cybersecurity","2026-11-10 10:12:30",false,"---\nname: analyzing-network-covert-channels-in-malware\ndescription: Detect and analyze covert communication channels used by malware, including\n  DNS tunneling, ICMP exfiltration, steganographic HTTP, and other protocol abuse\n  used for C2 and data exfiltration. Use when investigating suspicious DNS\u002FICMP\u002FHTTP\n  traffic patterns, hunting for hidden C2 channels in network captures, or attributing\n  exfiltration traffic to a known tunneling toolset.\ndomain: cybersecurity\nsubdomain: malware-analysis\ntags:\n- covert-channels\n- dns-tunneling\n- icmp-exfiltration\n- malware-analysis\n- network-forensics\n- c2-detection\n- data-exfiltration\nversion: '1.0'\nauthor: mahipal\nlicense: Apache-2.0\nd3fend_techniques:\n- File Metadata Consistency Validation\n- Certificate Analysis\n- Application Protocol Command Analysis\n- Content Format Conversion\n- File Content Analysis\nnist_csf:\n- DE.AE-02\n- RS.AN-03\n- ID.RA-01\n- DE.CM-01\nmitre_attack:\n- T1071.001\n- T1095\n- T1572\n- T1001\n---\n# Analyzing Network Covert Channels in Malware\n\n## Overview\n\nMalware uses covert channels to disguise C2 communication and data exfiltration within legitimate-looking network traffic. DNS tunneling encodes data in DNS queries and responses (used by tools like iodine, dnscat2, and malware families like FrameworkPOS). ICMP tunneling hides data in echo request\u002Freply payloads (icmpsh, ptunnel). HTTP covert channels embed C2 data in headers, cookies, or steganographic images. Protocol abuse exploits allowed protocols to bypass firewalls. DNS tunneling detection achieves 99%+ recall with modern ML-based approaches, though low-throughput exfiltration remains challenging. Palo Alto Unit42 tracked three major DNS tunneling campaigns (TrkCdn, SecShow, Savvy Seahorse) through 2024, showing the technique's continued prevalence.\n\n\n## When to Use\n\n- When investigating security incidents that require analyzing network covert channels in malware\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 `scapy`, `dpkt`, `dnslib`\n- Wireshark\u002Ftshark for PCAP analysis\n- Zeek (formerly Bro) for network monitoring\n- DNS query logging infrastructure\n- Understanding of DNS, ICMP, HTTP protocols at packet level\n\n## Workflow\n\n### Step 1: DNS Tunneling Detection\n\n```python\n#!\u002Fusr\u002Fbin\u002Fenv python3\n\"\"\"Detect DNS tunneling and covert channels in network traffic.\"\"\"\nimport sys\nimport json\nimport math\nfrom collections import Counter, defaultdict\n\ntry:\n    from scapy.all import rdpcap, DNS, DNSQR, DNSRR, IP, ICMP\nexcept ImportError:\n    print(\"pip install scapy\")\n    sys.exit(1)\n\n\ndef entropy(data):\n    if not data:\n        return 0\n    freq = Counter(data)\n    length = len(data)\n    return -sum((c\u002Flength) * math.log2(c\u002Flength) for c in freq.values())\n\n\ndef analyze_dns_tunneling(pcap_path):\n    \"\"\"Detect DNS tunneling indicators in PCAP.\"\"\"\n    packets = rdpcap(pcap_path)\n    domain_stats = defaultdict(lambda: {\n        \"queries\": 0, \"total_qname_len\": 0, \"subdomain_lengths\": [],\n        \"query_types\": Counter(), \"unique_subdomains\": set(),\n    })\n\n    for pkt in packets:\n        if pkt.haslayer(DNS) and pkt.haslayer(DNSQR):\n            qname = pkt[DNSQR].qname.decode('utf-8', errors='replace').rstrip('.')\n            qtype = pkt[DNSQR].qtype\n\n            parts = qname.split('.')\n            if len(parts) >= 3:\n                base_domain = '.'.join(parts[-2:])\n                subdomain = '.'.join(parts[:-2])\n\n                stats = domain_stats[base_domain]\n                stats[\"queries\"] += 1\n                stats[\"total_qname_len\"] += len(qname)\n                stats[\"subdomain_lengths\"].append(len(subdomain))\n                stats[\"query_types\"][qtype] += 1\n                stats[\"unique_subdomains\"].add(subdomain)\n\n    # Score domains for tunneling indicators\n    suspicious = []\n    for domain, stats in domain_stats.items():\n        if stats[\"queries\"] \u003C 5:\n            continue\n\n        avg_subdomain_len = (sum(stats[\"subdomain_lengths\"]) \u002F\n                             len(stats[\"subdomain_lengths\"]))\n        unique_ratio = len(stats[\"unique_subdomains\"]) \u002F stats[\"queries\"]\n\n        # Calculate subdomain entropy\n        all_subdomains = ''.join(stats[\"unique_subdomains\"])\n        sub_entropy = entropy(all_subdomains)\n\n        score = 0\n        reasons = []\n\n        if avg_subdomain_len > 30:\n            score += 30\n            reasons.append(f\"Long subdomains (avg {avg_subdomain_len:.0f} chars)\")\n        if unique_ratio > 0.9:\n            score += 25\n            reasons.append(f\"High uniqueness ({unique_ratio:.2%})\")\n        if sub_entropy > 4.0:\n            score += 25\n            reasons.append(f\"High entropy ({sub_entropy:.2f})\")\n        if stats[\"query_types\"].get(16, 0) > 10:  # TXT records\n            score += 20\n            reasons.append(f\"Many TXT queries ({stats['query_types'][16]})\")\n\n        if score >= 50:\n            suspicious.append({\n                \"domain\": domain,\n                \"score\": score,\n                \"queries\": stats[\"queries\"],\n                \"avg_subdomain_length\": round(avg_subdomain_len, 1),\n                \"unique_subdomains\": len(stats[\"unique_subdomains\"]),\n                \"subdomain_entropy\": round(sub_entropy, 2),\n                \"reasons\": reasons,\n            })\n\n    return sorted(suspicious, key=lambda x: -x[\"score\"])\n\n\ndef analyze_icmp_tunneling(pcap_path):\n    \"\"\"Detect ICMP tunneling in PCAP.\"\"\"\n    packets = rdpcap(pcap_path)\n    icmp_stats = defaultdict(lambda: {\"count\": 0, \"payload_sizes\": [], \"payloads\": []})\n\n    for pkt in packets:\n        if pkt.haslayer(ICMP) and pkt.haslayer(IP):\n            src = pkt[IP].src\n            dst = pkt[IP].dst\n            key = f\"{src}->{dst}\"\n\n            payload = bytes(pkt[ICMP].payload)\n            icmp_stats[key][\"count\"] += 1\n            icmp_stats[key][\"payload_sizes\"].append(len(payload))\n            if len(payload) > 64:\n                icmp_stats[key][\"payloads\"].append(payload[:100])\n\n    suspicious = []\n    for flow, stats in icmp_stats.items():\n        if stats[\"count\"] \u003C 5:\n            continue\n        avg_size = sum(stats[\"payload_sizes\"]) \u002F len(stats[\"payload_sizes\"])\n        if avg_size > 64 or stats[\"count\"] > 100:\n            suspicious.append({\n                \"flow\": flow,\n                \"packets\": stats[\"count\"],\n                \"avg_payload_size\": round(avg_size, 1),\n                \"reason\": \"Large\u002Ffrequent ICMP payloads suggest tunneling\",\n            })\n\n    return suspicious\n\n\nif __name__ == \"__main__\":\n    if len(sys.argv) \u003C 2:\n        print(f\"Usage: {sys.argv[0]} \u003Cpcap_file>\")\n        sys.exit(1)\n\n    print(\"[+] DNS Tunneling Analysis\")\n    dns_results = analyze_dns_tunneling(sys.argv[1])\n    for r in dns_results:\n        print(f\"  {r['domain']} (score: {r['score']})\")\n        for reason in r['reasons']:\n            print(f\"    - {reason}\")\n\n    print(\"\\n[+] ICMP Tunneling Analysis\")\n    icmp_results = analyze_icmp_tunneling(sys.argv[1])\n    for r in icmp_results:\n        print(f\"  {r['flow']}: {r['reason']}\")\n```\n\n## Validation Criteria\n\n- DNS tunneling detected via entropy, subdomain length, and query volume analysis\n- ICMP covert channels identified through payload size anomalies\n- Tunneling domains distinguished from legitimate CDN\u002Fcloud traffic\n- Data exfiltration volume estimated from captured traffic\n- C2 communication patterns and beaconing intervals extracted\n\n## References\n\n- [Palo Alto Unit42 - DNS Tunneling Campaigns](https:\u002F\u002Funit42.paloaltonetworks.com\u002Fthree-dns-tunneling-campaigns\u002F)\n- [Elastic - Detecting Covert Data Exfiltration](https:\u002F\u002Fwww.elastic.co\u002Fblog\u002Felastic-security-detecting-covert-data-exfiltration)\n- [Vectra AI - ICMP Tunnel Detection](https:\u002F\u002Fwww.vectra.ai\u002Fdetections\u002Ficmp-tunnel)\n- [MITRE ATT&CK T1071 - Application Layer Protocol](https:\u002F\u002Fattack.mitre.org\u002Ftechniques\u002FT1071\u002F)\n","# Analyzing Network Covert Channels in Malware\n\n## Overview\n\nMalware uses covert channels to disguise C2 communication and data exfiltration within legitimate-looking network traffic. DNS tunneling encodes data in DNS queries and responses (used by tools like iodine, dnscat2, and malware families like FrameworkPOS). ICMP tunneling hides data in echo request\u002Freply payloads (icmpsh, ptunnel). HTTP covert channels embed C2 data in headers, cookies, or steganographic images. Protocol abuse exploits allowed protocols to bypass firewalls. DNS tunneling detection achieves 99%+ recall with modern ML-based approaches, though low-throughput exfiltration remains challenging. Palo Alto Unit42 tracked three major DNS tunneling campaigns (TrkCdn, SecShow, Savvy Seahorse) through 2024, showing the technique's continued prevalence.\n\n\n## When to Use\n\n- When investigating security incidents that require analyzing network covert channels in malware\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 `scapy`, `dpkt`, `dnslib`\n- Wireshark\u002Ftshark for PCAP analysis\n- Zeek (formerly Bro) for network monitoring\n- DNS query logging infrastructure\n- Understanding of DNS, ICMP, HTTP protocols at packet level\n\n## Workflow\n\n### Step 1: DNS Tunneling Detection\n\n```python\n#!\u002Fusr\u002Fbin\u002Fenv python3\n\"\"\"Detect DNS tunneling and covert channels in network traffic.\"\"\"\nimport sys\nimport json\nimport math\nfrom collections import Counter, defaultdict\n\ntry:\n    from scapy.all import rdpcap, DNS, DNSQR, DNSRR, IP, ICMP\nexcept ImportError:\n    print(\"pip install scapy\")\n    sys.exit(1)\n\n\ndef entropy(data):\n    if not data:\n        return 0\n    freq = Counter(data)\n    length = len(data)\n    return -sum((c\u002Flength) * math.log2(c\u002Flength) for c in freq.values())\n\n\ndef analyze_dns_tunneling(pcap_path):\n    \"\"\"Detect DNS tunneling indicators in PCAP.\"\"\"\n    packets = rdpcap(pcap_path)\n    domain_stats = defaultdict(lambda: {\n        \"queries\": 0, \"total_qname_len\": 0, \"subdomain_lengths\": [],\n        \"query_types\": Counter(), \"unique_subdomains\": set(),\n    })\n\n    for pkt in packets:\n        if pkt.haslayer(DNS) and pkt.haslayer(DNSQR):\n            qname = pkt[DNSQR].qname.decode('utf-8', errors='replace').rstrip('.')\n            qtype = pkt[DNSQR].qtype\n\n            parts = qname.split('.')\n            if len(parts) >= 3:\n                base_domain = '.'.join(parts[-2:])\n                subdomain = '.'.join(parts[:-2])\n\n                stats = domain_stats[base_domain]\n                stats[\"queries\"] += 1\n                stats[\"total_qname_len\"] += len(qname)\n                stats[\"subdomain_lengths\"].append(len(subdomain))\n                stats[\"query_types\"][qtype] += 1\n                stats[\"unique_subdomains\"].add(subdomain)\n\n    # Score domains for tunneling indicators\n    suspicious = []\n    for domain, stats in domain_stats.items():\n        if stats[\"queries\"] \u003C 5:\n            continue\n\n        avg_subdomain_len = (sum(stats[\"subdomain_lengths\"]) \u002F\n                             len(stats[\"subdomain_lengths\"]))\n        unique_ratio = len(stats[\"unique_subdomains\"]) \u002F stats[\"queries\"]\n\n        # Calculate subdomain entropy\n        all_subdomains = ''.join(stats[\"unique_subdomains\"])\n        sub_entropy = entropy(all_subdomains)\n\n        score = 0\n        reasons = []\n\n        if avg_subdomain_len > 30:\n            score += 30\n            reasons.append(f\"Long subdomains (avg {avg_subdomain_len:.0f} chars)\")\n        if unique_ratio > 0.9:\n            score += 25\n            reasons.append(f\"High uniqueness ({unique_ratio:.2%})\")\n        if sub_entropy > 4.0:\n            score += 25\n            reasons.append(f\"High entropy ({sub_entropy:.2f})\")\n        if stats[\"query_types\"].get(16, 0) > 10:  # TXT records\n            score += 20\n            reasons.append(f\"Many TXT queries ({stats['query_types'][16]})\")\n\n        if score >= 50:\n            suspicious.append({\n                \"domain\": domain,\n                \"score\": score,\n                \"queries\": stats[\"queries\"],\n                \"avg_subdomain_length\": round(avg_subdomain_len, 1),\n                \"unique_subdomains\": len(stats[\"unique_subdomains\"]),\n                \"subdomain_entropy\": round(sub_entropy, 2),\n                \"reasons\": reasons,\n            })\n\n    return sorted(suspicious, key=lambda x: -x[\"score\"])\n\n\ndef analyze_icmp_tunneling(pcap_path):\n    \"\"\"Detect ICMP tunneling in PCAP.\"\"\"\n    packets = rdpcap(pcap_path)\n    icmp_stats = defaultdict(lambda: {\"count\": 0, \"payload_sizes\": [], \"payloads\": []})\n\n    for pkt in packets:\n        if pkt.haslayer(ICMP) and pkt.haslayer(IP):\n            src = pkt[IP].src\n            dst = pkt[IP].dst\n            key = f\"{src}->{dst}\"\n\n            payload = bytes(pkt[ICMP].payload)\n            icmp_stats[key][\"count\"] += 1\n            icmp_stats[key][\"payload_sizes\"].append(len(payload))\n            if len(payload) > 64:\n                icmp_stats[key][\"payloads\"].append(payload[:100])\n\n    suspicious = []\n    for flow, stats in icmp_stats.items():\n        if stats[\"count\"] \u003C 5:\n            continue\n        avg_size = sum(stats[\"payload_sizes\"]) \u002F len(stats[\"payload_sizes\"])\n        if avg_size > 64 or stats[\"count\"] > 100:\n            suspicious.append({\n                \"flow\": flow,\n                \"packets\": stats[\"count\"],\n                \"avg_payload_size\": round(avg_size, 1),\n                \"reason\": \"Large\u002Ffrequent ICMP payloads suggest tunneling\",\n            })\n\n    return suspicious\n\n\nif __name__ == \"__main__\":\n    if len(sys.argv) \u003C 2:\n        print(f\"Usage: {sys.argv[0]} \u003Cpcap_file>\")\n        sys.exit(1)\n\n    print(\"[+] DNS Tunneling Analysis\")\n    dns_results = analyze_dns_tunneling(sys.argv[1])\n    for r in dns_results:\n        print(f\"  {r['domain']} (score: {r['score']})\")\n        for reason in r['reasons']:\n            print(f\"    - {reason}\")\n\n    print(\"\\n[+] ICMP Tunneling Analysis\")\n    icmp_results = analyze_icmp_tunneling(sys.argv[1])\n    for r in icmp_results:\n        print(f\"  {r['flow']}: {r['reason']}\")\n```\n\n## Validation Criteria\n\n- DNS tunneling detected via entropy, subdomain length, and query volume analysis\n- ICMP covert channels identified through payload size anomalies\n- Tunneling domains distinguished from legitimate CDN\u002Fcloud traffic\n- Data exfiltration volume estimated from captured traffic\n- C2 communication patterns and beaconing intervals extracted\n\n## References\n\n- [Palo Alto Unit42 - DNS Tunneling Campaigns](https:\u002F\u002Funit42.paloaltonetworks.com\u002Fthree-dns-tunneling-campaigns\u002F)\n- [Elastic - Detecting Covert Data Exfiltration](https:\u002F\u002Fwww.elastic.co\u002Fblog\u002Felastic-security-detecting-covert-data-exfiltration)\n- [Vectra AI - ICMP Tunnel Detection](https:\u002F\u002Fwww.vectra.ai\u002Fdetections\u002Ficmp-tunnel)\n- [MITRE ATT&CK T1071 - Application Layer Protocol](https:\u002F\u002Fattack.mitre.org\u002Ftechniques\u002FT1071\u002F)\n",{"name":5,"tags":49,"author":50,"domain":43,"license":15,"version":51,"nist_csf":52,"subdomain":26,"description":7,"mitre_attack":57,"d3fend_techniques":62},[30,31,29,26,28,34,27],"mahipal","1.0",[53,54,55,56],"DE.AE-02","RS.AN-03","ID.RA-01","DE.CM-01",[58,59,60,61],"T1071.001","T1095","T1572","T1001",[63,64,65,66,67],"File Metadata Consistency Validation","Certificate Analysis","Application Protocol Command Analysis","Content Format Conversion","File Content Analysis","main","54a798831d2266a3ca61ce68a7acb80b81160d57","skills\u002Fanalyzing-network-covert-channels-in-malware",4395,"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":79,"prompt":89,"zipAvailable":90},[80,83,86],{"tool":81,"command":82},"npx","npx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill analyzing-network-covert-channels-in-malware",{"tool":84,"command":85},"bunx","bunx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill analyzing-network-covert-channels-in-malware",{"tool":87,"command":88},"pnpm","pnpm dlx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill analyzing-network-covert-channels-in-malware","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fanalyzing-network-covert-channels-in-malware\u002FSKILL.md",true,[92,98,103,109,113,117,123],{"path":93,"resourceType":94,"resourceTypeDesc":95,"sizeBytes":96,"sourceHtmlUrl":97},"assets\u002Ftemplate.md","ASSET","资产文件",425,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fanalyzing-network-covert-channels-in-malware\u002Fassets\u002Ftemplate.md",{"path":99,"resourceType":99,"resourceTypeDesc":100,"sizeBytes":101,"sourceHtmlUrl":102},"LICENSE","许可证",11255,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fanalyzing-network-covert-channels-in-malware\u002FLICENSE",{"path":104,"resourceType":105,"resourceTypeDesc":106,"sizeBytes":107,"sourceHtmlUrl":108},"references\u002Fapi-reference.md","REFERENCE","参考文档",2544,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fanalyzing-network-covert-channels-in-malware\u002Freferences\u002Fapi-reference.md",{"path":110,"resourceType":105,"resourceTypeDesc":106,"sizeBytes":111,"sourceHtmlUrl":112},"references\u002Fstandards.md",313,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fanalyzing-network-covert-channels-in-malware\u002Freferences\u002Fstandards.md",{"path":114,"resourceType":105,"resourceTypeDesc":106,"sizeBytes":115,"sourceHtmlUrl":116},"references\u002Fworkflows.md",472,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fanalyzing-network-covert-channels-in-malware\u002Freferences\u002Fworkflows.md",{"path":118,"resourceType":119,"resourceTypeDesc":120,"sizeBytes":121,"sourceHtmlUrl":122},"scripts\u002Fagent.py","SCRIPT","脚本",7739,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fanalyzing-network-covert-channels-in-malware\u002Fscripts\u002Fagent.py",{"path":124,"resourceType":119,"resourceTypeDesc":120,"sizeBytes":125,"sourceHtmlUrl":126},"scripts\u002Fanalyze.sh",2013,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Fweb-quality-audit\u002Fscripts\u002Fanalyze.sh",[128,155,179,206,232,259],{"skillNo":129,"name":130,"nameZh":6,"description":131,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":132,"categories":133,"keywords":135,"publishedAt":44},"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",[134],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[136,137,138,139,140,43,141,142,143,144,145,146,147,148,149,150,151,152,153,154],"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":156,"name":157,"nameZh":6,"description":158,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":159,"categories":160,"keywords":162,"publishedAt":44},"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",[161],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[163,151,164,165,140,166,43,143,167,168,169,170,171,172,173,174,175,176,177,178],"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":180,"name":181,"nameZh":6,"description":182,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":183,"categories":184,"keywords":189,"publishedAt":44},"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",[185,188],{"categoryNo":186,"categoryName":187,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[173,190,191,192,193,43,194,41,167,195,196,197,198,199,200,201,202,203,204,205],"backup","recovery","validating backup integrity for recovery","testing","infrastructure","compliance","hash-verification","incident-response","ransomware","cryptocurrency","detection","t1078","t1490","immutable","disaster-recovery","restore-testing",{"skillNo":207,"name":208,"nameZh":6,"description":209,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":210,"categories":211,"keywords":213,"publishedAt":44},"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",[212],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[214,215,216,43,217,218,219,220,221,222,223,224,225,226,227,228,170,229,230,231],"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":233,"name":234,"nameZh":6,"description":235,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":236,"categories":237,"keywords":239,"publishedAt":44},"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",[238],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[240,241,242,43,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258],"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":260,"name":261,"nameZh":6,"description":262,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":263,"categories":264,"keywords":266,"publishedAt":44},"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",[265],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[140,267,43,268,38,269,270,271,272,273,274,201,275,202,276,277,278,279,280,281],"triaging security incident","siem","ioc","soc","NIST-800-61","NIST","severity","edr","execution","analysis","classification","severity-classification","incident-triage","SANS-PICERL","SOC-operations"]