Use when about to take any outward or offensive action (request, payload, persistence, lateral movement, exfil, or feeding captured traffic to the model) — to decide detection footprint, cleanup, and secret redaction first
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Decide the detection footprint and cleanup before acting, not after. Every offensive technique in this repo pairs with the telemetry it generates and a detection signature — use that pairing to choose the quieter path and to know what you'll need to clean up.
python skills/coding-mastery/scripts/_lib/redact_headers.py < exchange.txt (or redact_text()),
so Authorization/Cookie/API-key/JWT values never land in context or the report.| Excuse | Reality |
|---|---|
| "OPSEC slows me down" | Unplanned noise/loot is how engagements get burned and how data leaks. |
| "It's an internal tool log" | Logs get shipped, shared, and indexed. Redact. |
| "Cleanup is a reporting-phase task" | You can't clean what you didn't track. Track at action time. |
OPSEC is part of every action, not a phase. Detection-awareness also strengthens the defensive value of the finding (you can tell the client exactly how to catch it).
아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.
아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.
Use when attacking a Windows Active Directory domain — Kerberos roasting/delegation, coercion + NTLM/Kerberos relay (CVE-2025-33073), ADCS ESC1-16 (EKUwu), ticket forgery & DCSync, dMSA BadSuccessor (CVE-2025-53779), BloodHound attack-path enumeration, domain dominance
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when red-teaming an agentic AI / LLM application — indirect & zero-click prompt injection, MCP tool poisoning, persistent memory poisoning, excessive-agency tool abuse, multi-turn jailbreaks, PyRIT/Garak/Promptfoo harnesses
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when attacking an AI/ML system or model — prompt injection & jailbreaks (Crescendo, Skeleton Key, Best-of-N), RAG/vector poisoning, agentic/MCP exploitation (CVE-2025-54136), ML supply-chain RCE (pickle CVE-2025-32434), model extraction / membership inference / adversarial suffixes (GCG)
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when building a client-side browser exploit — V8/JSC JIT type confusion to renderer R/W, V8 heap-sandbox escape, renderer-to-browser sandbox escape (Mojo IPC, GPU/Dawn/ANGLE), Electron/webview IPC abuse, 1-click RCE chains
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when attacking or auditing a CI/CD pipeline or software supply chain — pwn requests, poisoned pipeline execution, compromised/mutable-tag actions, dependency confusion, registry worms, runner backdoors, OIDC trust abuse, SLSA/provenance
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when attacking AWS/Azure/GCP cloud — IAM/identity privilege escalation, IMDS/metadata SSRF, Entra device-code & PRT theft, GCP impersonation chains, Kubernetes/container escape, IaC/CI-CD federation abuse
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.