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Skill · 已公开版本

stride-analysis-patterns · STRIDE威胁建模模板与问卷

按STRIDE六类系统梳理威胁、信任边界与缓解措施,附威胁模型文档模板和问卷生成代码。

先看看示例

作者提供的示例,实际效果取决于你的输入和使用工具。

输入

为含Web应用、API网关与数据库的系统生成STRIDE威胁清单与缓解措施。

输出

预期交付:分类威胁表、风险优先级、缓解建议与威胁模型报告。(编辑说明,非实际运行结果)

方法说明

---
name: stride-analysis-patterns
description: "Apply STRIDE methodology to systematically identify threats. Use when analyzing system security, conducting threat modeling sessions, or creating security documentation."
risk: safe
source: community
date_added: "2026-02-27"
---

# STRIDE Analysis Patterns

Systematic threat identification using the STRIDE methodology.

## Use this skill when

- Starting new threat modeling sessions
- Analyzing existing system architecture
- Reviewing security design decisions
- Creating threat documentation
- Training teams on threat identification
- Compliance and audit preparation

## Do not use this skill when

- The task is unrelated to stride analysis patterns
- You need a different domain or tool outside this scope

## Instructions

- Clarify goals, constraints, and required inputs.
- Apply relevant best practices and validate outcomes.
- Provide actionable steps and verification.
- If detailed examples are required, open `resources/implementation-playbook.md`.

## Resources

- `resources/implementation-playbook.md` for detailed patterns and examples.

## Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

## Modeling and scoring limits

The Python helpers produce prompts from user-supplied model data; they do not inspect software or establish actual vulnerabilities, control effectiveness or compliance. Impact×likelihood and all severities/deadlines in examples are illustrative ordinal policy choices, not CVSS or calibrated probabilities. Record evidence, assumptions, assets, trust boundaries, owners and residual-risk acceptance for each candidate. Equal numeric trust levels can still cross tenant/identity/administrative boundaries; model explicit boundary membership rather than relying solely on inequality. Encryption does not prevent authorized-but-abusive access, and authentication lockouts can create denial-of-service risks. Missing elements or unmodeled interaction types must be surfaced, not silently treated as no threats. Templates are partial independent blocks; validate imports, duplicate IDs and structured model inputs before use. Keep secret-bearing diagrams/logs private and do not run attack simulations without separate authorization.