「threat」の検索結果

Analyzes DNS query logs to detect data exfiltration via DNS tunneling, DGA domain communication, and covert C2 channels using entropy analysis, query volume anomalies, and subdomain length detection in SIEM platforms. Use when SOC teams need to identify DNS-based threats that bypass traditional network security controls.

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Perform Porter's Five Forces analysis — competitive rivalry, supplier power, buyer power, threat of substitutes, and threat of new entrants. Use when analyzing industry dynamics, assessing competitive forces, or evaluating market attractiveness.

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Expert guidance for proactive threat hunting in Google SecOps. Use when proactively hunting for threats, retroactively analyzing indicators of compromise (IoCs), performing prevalence searches across enterprise events, hunting for MITRE ATT&CK techniques, or detecting behavioral and statistical outliers using UDM queries. Don't use for incoming alert triage (use secops-triage), active incident response and timeline deep-dives on a known breach (use secops-investigate), or detection rule authoring (use secops-detection-engineering).

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Author, validate, test, and deploy YARA-L 2.0 detection rules and evaluate end-to-end detection coverage gaps in Google SecOps. Use when writing new detection rules, tuning existing rules, validating syntax, testing logic against historical telemetry, or evaluating detection coverage against threat intelligence blogs, CVE disclosures, and Threat Detection Opportunities (TDOs) using synthetic UDM events and long-running coverage analysis. Don't use for alert triage (use secops-triage), deep forensic event reconstruction on infected hosts (use secops-investigate), or case management operations (use secops-cases).

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Queries and retrieves active security findings, external exposures, toxic combinations, vulnerabilities, threats, and sensitive data risks from Security Command Center on Google Cloud. Use when retrieving details for a security finding by its name, validating finding scope (e.g., verifying findingClass is TOXIC_COMBINATION, VULNERABILITY, EXTERNAL_EXPOSURE, or THREAT), or fetching finding details for triage. Don't use to draft remediations, apply patches, or execute configurations.

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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).

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Execute any of 240+ specialized prompt patterns natively across Extraction, Summarization, Analysis, Creation, Improvement, Security, Rating. Common: extract_wisdom, create_threat_model, analyze_claims, improve_writing, review_code, mermaid, youtube_summary. CLI used only for YouTube transcript (-y) and URL fallback (-u). Two workflows: ExecutePattern, UpdatePatterns. USE WHEN fabric, fabric pattern, run fabric, update patterns, threat model, analyze claims, improve writing, review code, mermaid, STRIDE, sigma rules. NOT FOR multi-agent investigation (Research) or content-adaptive extraction (ExtractWisdom).

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Static source-code vulnerability scan. Reads a target directory (and THREAT_MODEL.md if present), spawns parallel review subagents per focus area, and writes VULN-FINDINGS.json + .md for /triage to consume. Read-only — no building, running, or network. For execution-verified crashes, use vuln-pipeline instead. Use when asked to "scan for vulns", "review this code for security issues", "find bugs in <dir>", or as the step between /threat-model and /triage.

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Perform language and framework specific security best-practice reviews and suggest improvements. Use when the user explicitly requests security best practices guidance, a security review or report, or secure-by-default coding help. Supports Python, JavaScript/TypeScript, and Go. Do NOT use for general code review, debugging, threat modeling (use security-threat-model), or non-security tasks.

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Analyze git repositories to build a security ownership topology (people-to-file), compute bus factor and sensitive-code ownership, and export CSV/JSON for graph databases and visualization. Use when the user explicitly wants a security-oriented ownership or bus-factor analysis grounded in git history (for example: orphaned sensitive code, security maintainers, CODEOWNERS reality checks for risk, sensitive hotspots, or ownership clusters). Do NOT use for general maintainer lists, non-security ownership questions, or threat modeling (use security-threat-model).

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Delegate threat-intelligence research and updates to AgentSec, then validate the guide and landing mirrors.

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FAIR-aligned exposure quantification: turns a pile of recon findings into a defensible 0-100 + A-F org risk score (Likelihood x Impact, three ownership-aware factors: exposure/threat/impact), an ownership + proof demotion cap so unproven or weakly-owned findings can't inflate the number, a $-denominated FAIR loss-magnitude estimate (IBM/Ponemon per-record cost bands, cross-source record dedup, threat-factor annualization), attack-path amplification (curated red-team chain catalog + generic graph-walk engine, with a kill-chain vs shared-fate honesty gate), and a board-ready one-pager deliverable (hero $ + letter grade + top-3 findings + top attack path + the ask). Extends osint-methodology's severity rubric and client deliverable templates with quantification. Passive analysis only -- operates on findings already collected, no target traffic, no API keys. Use when asked to score risk, quantify exposure, estimate breach cost, build a board report, translate technical findings to dollars, or explain why a grade or dollar figure came out the way it did.

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Identifies what could go wrong in a system before it is built or changed — the assets worth attacking, the entry points, the trust boundaries, and the controls that actually address the realistic threats. Use this when designing a feature or system, when a change touches authentication, data handling, payments, or external input, before a security review, or when deciding which security work is worth doing at all.

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Apply STRIDE methodology to systematically identify threats. Use when analyzing system security, conducting threat modeling sessions, or creating security documentation.

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Map identified threats to appropriate security controls and mitigations. Use when prioritizing security investments, creating remediation plans, or validating control effectiveness.

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Derive security requirements from threat models and business context. Use when translating threats into actionable requirements, creating security user stories, or building security test cases.

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Expert malware analyst specializing in defensive malware research, threat intelligence, and incident response. Masters sandbox analysis, behavioral analysis, and malware family identification. Handles static/dynamic analysis, unpacking, and IOC extraction. Use PROACTIVELY for malware triage, threat hunting, incident response, or security research.

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Use when an AEJ: Economic Policy manuscript's headline policy estimate needs to be shown stable and credible against specification, sample, inference, and identification threats. Organizes the robustness program by threat-to-the-policy-conclusion; it does not design the primary identification or write exhibits.

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Infrastructure-first security audit across 14 phases: attack surface mapping, secrets archaeology (git history), supply chain analysis, CI/CD pipeline security, LLM/AI threat detection, OWASP Top 10, STRIDE threat modeling. Confidence gates, exploit scenarios, parallel verification, trend tracking. Read-only.

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。

Apply STRIDE methodology to systematically identify threats. Use when analyzing system security, conducting threat modeling sessions, or creating security documentation.

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。

MCP threat-model artifact for a scaffolded harness. Reports allowed/denied tools, dangerous permissions count, secrets reachability, network/shell/file-write grants, default-deny posture. Verdict: clean (exit 0) / medium (exit 1) / high (exit 2). The 'enterprise gold' artifact for PR + compliance review.

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Use for blue-team threat hunting, detection engineering with Sigma/YARA, SIEM query design, and incident detection validation.

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Use to re-verify the threat mitigations of a phase that already shipped, or when the user asks to audit SECURITY.md. Audits an existing SECURITY.md, runs from a PLAN.md threat model, or exits if the phase never ran.

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Conducts hypothesis-driven, evidence-preserving threat hunts with bounded negative conclusions and telemetry-gap handling. Use for hunt hypotheses, scoped offline analysis, or analytic follow-up; not unscoped scanning, malware execution, or generic intelligence summaries.

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。