[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6ADNHEWDBWB9JYQG865G2EZ":3,"skill-same:01M6ADNHEWDBWB9JYQG865G2EZ":93},{"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":31,"linkOnly":32,"markdownContent":33,"markdownBody":34,"frontMatter":35,"refName":48,"commitSha":49,"skillDirectory":50,"forksCount":51,"repositoryHtmlUrl":52,"repositoryHomepageUrl":53,"ownerHtmlUrl":54,"ownerBlogUrl":55,"ownerDescription":56,"updatedAt":57,"install":58,"resources":71},"01M6ADNHEWDBWB9JYQG865G2EZ","analyzing-heap-spray-exploitation",null,"Detect and analyze heap spray attacks in memory dumps using Volatility3 plugins to identify NOP sled patterns, shellcode landing zones, and suspicious large allocations in process virtual address space.","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-heap-spray-exploitation\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30],"analyzing heap spray exploitation","suspicious","memory","cybersecurity","plugins","forensics","malware","malware-analysis","memory-forensics","heap-spray","volatility3","exploit-analysis","2026-11-10 10:12:30",false,"---\nname: analyzing-heap-spray-exploitation\ndescription: Detect and analyze heap spray attacks in memory dumps using Volatility3\n  plugins to identify NOP sled patterns, shellcode landing zones, and suspicious large\n  allocations in process virtual address space.\ndomain: cybersecurity\nsubdomain: malware-analysis\ntags:\n- malware-analysis\n- memory-forensics\n- heap-spray\n- volatility3\n- exploit-analysis\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- T1203\n- T1059.007\n- T1106\n---\n# Analyzing Heap Spray Exploitation\n\n## Overview\n\nHeap spraying is an exploitation technique that fills large regions of a process's heap with attacker-controlled data (typically NOP sleds followed by shellcode) to increase the reliability of code execution exploits. This skill covers detecting heap spray artifacts in memory dumps using Volatility3's malfind, vadinfo, and memmap plugins, identifying suspicious contiguous memory allocations, scanning for NOP sled patterns (0x90, 0x0c0c0c0c), and extracting embedded shellcode for analysis.\n\n\n## When to Use\n\n- When investigating security incidents that require analyzing heap spray exploitation\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 `volatility3` framework installed\n- Memory dump file (.raw, .vmem, .dmp format)\n- Understanding of virtual memory layout and VAD (Virtual Address Descriptor) trees\n- Familiarity with common shellcode patterns and NOP sled encodings\n\n## Steps\n\n### Step 1: Identify Suspicious Processes\nUse Volatility3 windows.malfind to scan for processes with executable injected memory regions.\n\n### Step 2: Analyze VAD Entries\nExamine VAD tree entries using windows.vadinfo for large contiguous allocations with RWX permissions.\n\n### Step 3: Scan for NOP Sled Patterns\nSearch suspicious memory regions for NOP sled signatures (0x90 sequences, 0x0c0c0c0c patterns).\n\n### Step 4: Extract and Analyze Shellcode\nDump suspicious memory regions and identify shellcode using byte pattern analysis.\n\n## Expected Output\n\nJSON report with suspicious processes, heap spray indicators, NOP sled locations, memory region sizes, and extracted shellcode hashes.\n","# Analyzing Heap Spray Exploitation\n\n## Overview\n\nHeap spraying is an exploitation technique that fills large regions of a process's heap with attacker-controlled data (typically NOP sleds followed by shellcode) to increase the reliability of code execution exploits. This skill covers detecting heap spray artifacts in memory dumps using Volatility3's malfind, vadinfo, and memmap plugins, identifying suspicious contiguous memory allocations, scanning for NOP sled patterns (0x90, 0x0c0c0c0c), and extracting embedded shellcode for analysis.\n\n\n## When to Use\n\n- When investigating security incidents that require analyzing heap spray exploitation\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 `volatility3` framework installed\n- Memory dump file (.raw, .vmem, .dmp format)\n- Understanding of virtual memory layout and VAD (Virtual Address Descriptor) trees\n- Familiarity with common shellcode patterns and NOP sled encodings\n\n## Steps\n\n### Step 1: Identify Suspicious Processes\nUse Volatility3 windows.malfind to scan for processes with executable injected memory regions.\n\n### Step 2: Analyze VAD Entries\nExamine VAD tree entries using windows.vadinfo for large contiguous allocations with RWX permissions.\n\n### Step 3: Scan for NOP Sled Patterns\nSearch suspicious memory regions for NOP sled signatures (0x90 sequences, 0x0c0c0c0c patterns).\n\n### Step 4: Extract and Analyze Shellcode\nDump suspicious memory regions and identify shellcode using byte pattern analysis.\n\n## Expected Output\n\nJSON report with suspicious processes, heap spray indicators, NOP sled locations, memory region sizes, and extracted shellcode hashes.\n",{"name":5,"tags":36,"author":37,"domain":22,"license":15,"version":38,"nist_csf":39,"subdomain":26,"description":7,"mitre_attack":44},[26,27,28,29,30],"mahipal","1.0",[40,41,42,43],"DE.AE-02","RS.AN-03","ID.RA-01","DE.CM-01",[45,46,47],"T1203","T1059.007","T1106","main","54a798831d2266a3ca61ce68a7acb80b81160d57","skills\u002Fanalyzing-heap-spray-exploitation",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":59,"prompt":69,"zipAvailable":70},[60,63,66],{"tool":61,"command":62},"npx","npx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill analyzing-heap-spray-exploitation",{"tool":64,"command":65},"bunx","bunx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill analyzing-heap-spray-exploitation",{"tool":67,"command":68},"pnpm","pnpm dlx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill analyzing-heap-spray-exploitation","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fanalyzing-heap-spray-exploitation\u002FSKILL.md",true,[72,78,83,87],{"path":73,"resourceType":74,"resourceTypeDesc":75,"sizeBytes":76,"sourceHtmlUrl":77},"agents\u002Fopenai.yaml","REFERENCE","参考文档",247,"https:\u002F\u002Fgithub.com\u002Fnodetool-ai\u002Fnodetool\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fgrill-with-docs\u002Fagents\u002Fopenai.yaml",{"path":79,"resourceType":79,"resourceTypeDesc":80,"sizeBytes":81,"sourceHtmlUrl":82},"LICENSE","许可证",11255,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fanalyzing-heap-spray-exploitation\u002FLICENSE",{"path":84,"resourceType":74,"resourceTypeDesc":75,"sizeBytes":85,"sourceHtmlUrl":86},"references\u002Fapi-reference.md",2220,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fanalyzing-heap-spray-exploitation\u002Freferences\u002Fapi-reference.md",{"path":88,"resourceType":89,"resourceTypeDesc":90,"sizeBytes":91,"sourceHtmlUrl":92},"scripts\u002Fagent.py","SCRIPT","脚本",8294,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fanalyzing-heap-spray-exploitation\u002Fscripts\u002Fagent.py",[94,125,149,177,203,230],{"skillNo":95,"name":96,"nameZh":6,"description":97,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":98,"categories":99,"keywords":105,"publishedAt":31},"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",[100],{"categoryNo":101,"categoryName":102,"taxonomyType":103,"taxonomyTypeDesc":104},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[106,107,108,109,110,22,111,112,113,114,115,116,117,118,119,120,121,122,123,124],"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":126,"name":127,"nameZh":6,"description":128,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":129,"categories":130,"keywords":132,"publishedAt":31},"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",[131],{"categoryNo":101,"categoryName":102,"taxonomyType":103,"taxonomyTypeDesc":104},[133,121,134,135,110,136,22,113,137,138,139,140,141,142,143,144,145,146,147,148],"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":150,"name":151,"nameZh":6,"description":152,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":153,"categories":154,"keywords":159,"publishedAt":31},"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",[155,158],{"categoryNo":156,"categoryName":157,"taxonomyType":103,"taxonomyTypeDesc":104},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":101,"categoryName":102,"taxonomyType":103,"taxonomyTypeDesc":104},[143,160,161,162,163,22,164,165,137,166,167,168,169,170,171,172,173,174,175,176],"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":178,"name":179,"nameZh":6,"description":180,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":181,"categories":182,"keywords":184,"publishedAt":31},"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",[183],{"categoryNo":101,"categoryName":102,"taxonomyType":103,"taxonomyTypeDesc":104},[185,186,187,22,188,189,190,191,192,193,194,195,196,197,198,199,140,200,201,202],"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":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":31},"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",[209],{"categoryNo":101,"categoryName":102,"taxonomyType":103,"taxonomyTypeDesc":104},[211,212,213,22,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229],"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":231,"name":232,"nameZh":6,"description":233,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":234,"categories":235,"keywords":237,"publishedAt":31},"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",[236],{"categoryNo":101,"categoryName":102,"taxonomyType":103,"taxonomyTypeDesc":104},[110,238,22,239,240,241,242,243,244,245,246,172,247,173,248,249,250,251,252,253],"triaging security incident","siem","file","ioc","soc","NIST-800-61","NIST","severity","edr","execution","analysis","classification","severity-classification","incident-triage","SANS-PICERL","SOC-operations"]