「attack」の検索結果

Parses Kubernetes API server audit logs (JSON lines) to detect exec-into-pod, secret access, RBAC modifications, privileged pod creation, and anonymous API access, and builds SIEM detection rules from the event patterns. Use when investigating a suspected cluster compromise, reconstructing what an attacker did through the API server, or writing Kubernetes-specific detection content. Keywords: audit policy, audit log, kube-apiserver, exec into pod, RBAC change, anonymous access, detection rules. Do not use for syscall-level detection inside a running container - use detecting-container-runtime-threats-with-falco. '

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 planning, running, or learning from chaos engineering experiments. Triggers on "chaos experiment", "fault injection", "gameday", "resilience test", "blast radius", "steady state", "abort criteria", "Chaos Toolkit", "Chaos Mesh", "Litmus", "Gremlin", "AWS FIS", or any deliberate failure-injection question. Ships experiment designer, blast-radius calculator, and postmortem generator (all stdlib Python), 4 references on chaos principles + experiment design + attack taxonomy + tooling landscape, and a /chaos-experiment slash command. Composes with feature-flags-architect (kill switches as abort triggers) and kubernetes-operator (common chaos targets).

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 planning or executing authorized red team engagements, attack path analysis, or offensive security simulations. Covers MITRE ATT&CK kill-chain planning, technique scoring, choke point identification, OPSEC risk assessment, and crown jewel targeting.

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。

Repository-grounded threat modeling that enumerates trust boundaries, assets, attacker capabilities, abuse paths, and mitigations, and writes a concise Markdown threat model. Trigger only when the user explicitly asks to threat model a codebase or path, enumerate threats/abuse paths, or perform AppSec threat modeling. Do not trigger for general architecture summaries, code review, or non-security design work.

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。

Remediates Google Cloud Security Command Center findings, including IAM permission fixes, cloud resource misconfigurations, vulnerabilities, and Toxic Combinations. Use when asked to fix, remediate, or mitigate a Security Command Center finding or address attack paths. Don't use for general IAM policy querying without a Security Command Center finding. For runtime threat detections, this skill provides containment and investigation guidance rather than automated configuration fixes.

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。

Automates the end-to-end detection engineering workflow in Google SecOps using MCP tools. Use when fetching threat intelligence from blogs, generating Threat Detection Opportunities (TDOs), simulating attacker behavior with synthetic UDM events, evaluating rule coverage, and deploying gap-closing rules. Don't use for standalone YARA-L 2.0 rule authoring/tuning (use secops-detection-engineering), threat hunting, or SOC investigation.

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。

Persistent world-model harness that stress-tests ideas, strategies, and investments against 11 time horizons from 6 months to 50 years, each a deep analysis of geopolitics, tech, economics, society, and security, in Fast/Standard/Deep tiers. USE WHEN threat model, world model, test idea, future analysis, time horizon, stress test against future, long-term risk. NOT FOR single-shot idea attack (use RedTeam).

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。

Audits any AI instruction set for over-prompting using the core test — would a smarter model make this rule unnecessary? Applies Five Questions to every rule (Claude already does this? Contradiction? Redundant? One-off fix? Vague?) then classifies as CUT/RESOLVE/MERGE/EVALUATE/SHARPEN/MOVE/KEEP. Workflows: Audit (full system, token savings), QuickCheck (single file). Principle: less scaffolding = better output. USE WHEN BPE, bitter pill, audit setup, over-prompting, trim instructions, dead weight, simplify setup, clean up CLAUDE.md. NOT FOR attacking logical flaws in ideas (use RedTeam).

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。

Multi-agent collaborative debate producing visible round-by-round transcripts with real intellectual friction — members are topic-briefed custom agents, run as a 3-round DEBATE or a 1-round QUICK check, to find the best path. USE WHEN council, debate, multiple perspectives, weigh options, deliberate, get different views, what would experts say, pros and cons. NOT FOR pure adversarial attack (use RedTeam).

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。

This skill should be used when writing, enhancing, or evaluating the launch prompt for a long-running autonomous agent or a parallel multi-agent orchestration attacking a hard problem: pseudo-formal task briefs that define terms and an exact success predicate linguistically, enumerate non-counting outcomes, set persistence rules with explicit stop and return conditions and effort floors, manage a diverse portfolio of parallel approaches with an approach registry and blocked-route bookkeeping, and gate the return on adversarial audit. Route agent topology and coordination protocols to multi-agent-patterns, runtime control surfaces and loop governance to harness-engineering, evaluator and quality-gate construction to evaluation, judge design to advanced-evaluation, and compaction or memory mechanics to context-compression and memory-systems.

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。

External research synthesis from Trail of Bits, SlowMist, ConsenSys, Immunefi, and Cyfrin. Use this for advanced audit methodology, Echidna/Medusa fuzzing setup, Slither custom detector writing, attack pattern deep dives, or the 4-phase learning roadmap.

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。

ZKsync Era (Immunefi) completed hunt — 0 findings after exhaustive 5-session audit. Use as a DEFENSE STUDY — learn what makes a protocol unhuntable, which patterns block all 10 bug classes, and when to abandon a target. Contains architecture breakdown, 25 tested attack vectors, and pre-dive scoring refinements for large L1 bridge protocols.

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。

Complete reference for all 10 DeFi smart contract bug classes. Use this when hunting for specific vulnerability types, need attack patterns for accounting desync, access control, incomplete path, off-by-one, oracle manipulation, ERC4626 vaults, reentrancy, flash loans, signature replay, or proxy/upgrade bugs.

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。

Repository-grounded threat modeling that enumerates trust boundaries, assets, attacker capabilities, abuse paths, and mitigations, and writes a concise Markdown threat model. Trigger only when the user explicitly asks to threat model a codebase or path, enumerate threats/abuse paths, or perform AppSec threat modeling. Do not trigger for general architecture summaries, code review, or non-security design work.

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。

Trigger: new issue, bug report, triage, backlog, issue flood, community report, root cause, dead-end, blocked user. Attack issues by root class, never one-by-one; fixes must shrink the system, not grow it.

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。

Incident response workup for a lead in hand — an alert, an IOC, or a /dnr-hunt finding. Scopes the lead across the logs, verdicts whether the attack succeeded, quantifies blast radius, confirms root cause in source with a local PoC, and writes a proposed (never executed) containment/remediation/recovery plan. Use when asked to "respond to this alert", "work this incident", "how bad is this", or "run dnr-respond". /dnr-hunt is the no-alert entry to the same track.

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。

Build a threat model for a target codebase. Three modes: "interview" walks an application owner through the four-question framework and produces a threat model from their answers; "bootstrap" derives a threat model from the code plus past vulnerabilities (CVEs, git history, pentest reports) when no owner is available; "bootstrap-then-interview" chains the two when both owner and codebase are present. All write THREAT_MODEL.md in a shared schema. Use when asked to "threat model", "build a threat model", "map the attack surface", or "what should we be worried about in this codebase".

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。

Measures timing side channels in cryptographic implementations by running them, using dudect for statistical analysis and Timecop over Valgrind for dynamic tracing. Covers the formal, symbolic, dynamic, and statistical tool categories and how to read a result. Use when testing whether a running implementation is constant-time, measuring timing variance on a compiled binary, or investigating a suspected timing attack. Not for statically inspecting compiler output — the constant-time-analysis plugin covers that.

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。

Runs full Trailmark structural analysis by building a graph, running `preanalysis()`, and reporting hotspots, taint, blast radius, privilege boundaries, attack surface, and version-gated Trailmark 0.4+/0.5+ data such as proxy counts, subgraph edges, type/reference summaries, and entrypoint attributes. Use when vivisect needs detailed structural data for a target. Triggers: structural analysis, blast radius, taint analysis, complexity hotspots, proxy nodes, type references.

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。

Builds and queries multi-language source and binary code graphs for security analysis. Includes pre-analysis passes for blast radius, taint propagation, privilege boundaries, entry point enumeration, proxy/unresolved-call tracking, type/reference queries, structural traversal, graph diffs, audit augmentation, declared cross-language/FFI/external links via `.trailmark/links.toml`, and SQL schema graphs. Use when analyzing call paths, mapping attack surface, finding complexity hotspots, enumerating entry points, tracing taint propagation, measuring blast radius, importing SARIF/weAudit/binary findings, linking source graphs across language or RPC boundaries, or building a code graph for audit prioritization. Feature-gate version-specific Trailmark APIs before using them; prefer `trailmark.parse.detect_languages()` or `--language auto` when the target language is unknown or polyglot.

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。

Translates Mermaid sequenceDiagrams describing cryptographic protocols into ProVerif formal verification models (.pv files). Use when generating a ProVerif model, formally verifying a protocol, converting a Mermaid diagram to ProVerif, verifying protocol security properties (secrecy, authentication, forward secrecy), checking for replay attacks, or producing a .pv file from a sequence diagram.

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。

Scans Algorand smart contracts for 11 common vulnerabilities including rekeying attacks, unchecked transaction fees, missing field validations, and access control issues. Use when auditing Algorand projects (TEAL/PyTeal).

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。

Bluetooth Low Energy (BLE) attack methodology — GATT enumeration, characteristic read/write without auth, pairing downgrade (Just Works forced), LE Secure Connections bypass, MITM via active relay, sniffing with Sniffle (TI CC1352) / Ubertooth / Frontline, encryption key extraction (LE Legacy Pairing crackable, LE Secure Connections strong), proximity authentication abuse (cars, locks), and companion-app trust analysis. Use for IoT BLE devices, smart locks, fitness trackers, medical devices, BLE beacons, or any device pairing over BLE.

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。

Comprehensive OSINT methodology skill for offensive security, red team intelligence gathering, and bug bounty reconnaissance. Covers domain recon, email harvesting, social media profiling, GitHub/code leaks, Shodan/Censys enumeration, breach data lookup, employee profiling, infrastructure mapping, cryptocurrency tracing, geospatial intelligence, and AI-assisted analysis workflows. Use when performing reconnaissance against a target domain or organization, investigating a person or entity, tracing cryptocurrency flows, geolocating images or events, or building an attack-surface map.

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。