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
インストールする前に、エージェントに与えられる指示の中身を確認できます。
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).
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
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
日本語の概要は準備中です。原文の説明を表示しています。
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
日本語の概要は準備中です。原文の説明を表示しています。
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)
日本語の概要は準備中です。原文の説明を表示しています。
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
日本語の概要は準備中です。原文の説明を表示しています。
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
日本語の概要は準備中です。原文の説明を表示しています。
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
日本語の概要は準備中です。原文の説明を表示しています。