threat-modeling

name	threat-modeling
description	Proactive threat identification using structured frameworks (STRIDE, PASTA). Models data flows, trust boundaries, and threat actors to prioritize security testing and contextualize findings with business risk.
applyTo	**/*

Threat Modeling

Purpose

Proactively identify threats before or during security assessment using structured frameworks. While other skills are reactive (find vulnerabilities), threat modeling maps the system's design, data flows, trust boundaries, and threat actors to prioritize testing and contextualize findings with business risk. Produces threat models that guide all downstream assessment skills.

Triggers

• Start of new engagement (before detailed testing)
• New system architecture or major feature being designed
• User requests threat model
• Complex system where testing priorities are unclear
• Findings need business risk contextualization
• Architecture review or design security assessment

Required Inputs

Input	Description	Required
governance_context	Active engagement governance record	Yes
system_description	Architecture docs, diagrams, or code structure	Yes
data_classification	What sensitive data the system handles	Recommended
threat_actors	Who might attack (external, insider, competitor, nation-state)	Recommended
business_context	Business value, regulatory requirements, risk appetite	Recommended
existing_controls	Current security measures in place	Recommended

Workflow

1. System Decomposition — Break system into components:

   • External entities (users, third-party services, external APIs)
   • Processes (application components, microservices, serverless functions)
   • Data stores (databases, caches, file storage, message queues)
   • Data flows (between each component, protocols used)
   • Trust boundaries (network zones, authentication boundaries, privilege levels)

2. Data Flow Diagram (DFD) — Create structured representation:

   Level 0: Context diagram (system as single process, external entities)
   Level 1: Major subsystems and data stores
   Level 2: Individual processes and detailed data flows
   

   For each data flow, document:

   • Source → Destination
   • Data type and sensitivity classification
   • Protocol and encryption
   • Authentication and authorization mechanism
   • Trust boundary crossings

3. STRIDE Analysis — For each component and data flow:

   Threat	Question	Example
   Spoofing	Can an attacker impersonate a legitimate entity?	Stolen JWT, forged certificate, session hijack
   Tampering	Can data be modified in transit or at rest?	Man-in-the-middle, SQL injection, parameter tampering
   Repudiation	Can actors deny their actions?	Missing audit logs, unsigned transactions
   Information Disclosure	Can sensitive data be exposed?	Unencrypted traffic, verbose errors, directory listing
   Denial of Service	Can the system be made unavailable?	Resource exhaustion, algorithmic complexity, unbounded queries
   Elevation of Privilege	Can an attacker gain higher access?	IDOR, missing authorization, kernel exploit, container escape

4. PASTA Analysis (if business risk context available):

   Stage	Activity
   1. Define Objectives	Business objectives, security requirements, compliance mandates
   2. Define Technical Scope	Architecture, technologies, dependencies, infrastructure
   3. Application Decomposition	DFD, trust boundaries, entry points, assets
   4. Threat Analysis	Threat intelligence, relevant attack patterns, known TTPs
   5. Vulnerability Analysis	Map to known weaknesses, correlate with CVEs
   6. Attack Modeling	Attack trees, kill chains for highest-risk scenarios
   7. Risk & Impact Analysis	Quantify risk: likelihood × impact, prioritize by business value

5. Threat Actor Profiling — For each relevant actor:

   Actor	Capability	Motivation	Typical TTPs
   Script kiddie	Low	Notoriety, fun	Public exploits, automated scanners
   Opportunistic attacker	Medium	Financial gain	Known CVEs, credential stuffing, phishing
   AI-augmented attacker	Medium→High	Financial gain, scale	AI-generated malware, AI-powered phishing (+1,265%), deepfake social engineering, AI-assisted recon, AI wordlist generation
   Organized crime	High	Financial gain, ransomware	Spear phishing, AI deepfakes, voice cloning, zero-days, persistence
   Insider threat	Medium-High	Revenge, financial, espionage	Authorized access abuse, data exfiltration, AI tool misuse
   Competitor	Medium-High	Competitive advantage	Targeted attacks, IP theft, AI model extraction
   Nation-state	Very High	Espionage, disruption	APT, supply chain, AI-assisted exploitation, zero-days, infrastructure

6. Attack Tree Construction — For top threats:

   • Define attacker goal (root node)
   • Map alternative paths to achieve goal (OR nodes)
   • Map required conditions for each path (AND nodes)
   • Annotate with: difficulty, detectability, existing controls
   • Identify: cheapest/easiest path = highest priority for testing

7. Risk Prioritization — Rank threats by:

   • Likelihood (actor capability × exploitability × exposure)
   • Impact (confidentiality, integrity, availability, financial, regulatory)
   • Existing control effectiveness
   • Detection capability

8. AI/LLM Component Threat Analysis — When system includes AI/LLM features:

   Apply STRIDE specifically to AI components:

   Component	Threat	Description	Skill
   LLM chatbot / API	Spoofing	Impersonate model or system prompt	llm-and-ai-security
   LLM prompt pipeline	Tampering	Prompt injection (direct / indirect) changes behavior	llm-and-ai-security
   AI agentic tools	Elevation of Privilege	Injected prompt calls unauthorized tool	llm-and-ai-security
   RAG knowledge base	Tampering	Attacker poisons vector store	llm-and-ai-security
   Model output sink	Tampering	Insecure output handling → XSS, SQLi, RCE	secure-code-review
   AI API key	Information Disclosure	Key exposed client-side or in responses	dependency-and-secret-audit
   AI training data	Tampering	Data poisoning / adversarial training inputs	llm-and-ai-security
   AI-integrated extension	Elevation of Privilege	IDOR via extension (e.g., Google Bard/Gemini pattern)	api-security-review

   Also consider AI-augmented attacker paths:

   • AI-powered phishing targeting users of the system
   • AI deepfake/voice clone for social engineering against staff
   • AI-assisted recon and wordlist generation against the target
   • AI-generated malware adapting to the target environment

9. Testing Priority Map — Translate threats to testing actions:

   • Map each high-priority threat to specific assessment skill(s)
   • Define test scenarios that validate each threat
   • Prioritize assessment skill execution order based on threat ranking
   • Identify gaps: threats that current skills can't address

10. Output — Deliver threat model document and testing priority guide

Allowed Actions

• Analyze architecture documents and diagrams
• Review source code structure for system decomposition
• Create data flow diagrams from available information
• Perform STRIDE/PASTA analysis
• Profile threat actors relevant to target
• Construct attack trees
• Prioritize threats by risk
• Map threats to assessment skills and test scenarios
• Identify security control gaps

Forbidden Actions

• Perform active testing during threat modeling (this is analysis only)
• Make assumptions about system behavior without documenting them
• Skip threat categories because "they probably don't apply"
• Produce generic threat models not tailored to the specific system
• Ignore business context when available
• Dismiss low-likelihood threats without documenting reasoning

Output Format

Threat Model

## Threat Model: [System Name]

### System Overview
[2-3 sentences: what the system does, key data, users]

### Data Flow Summary

| # | Flow | Source → Destination | Data | Protocol | Auth | Trust Boundary Crossing |
|---|------|---------------------|------|----------|------|------------------------|
| 1 | User login | Browser → API | Credentials | HTTPS | None → Session | External → DMZ |
| 2 | API to DB | API Server → PostgreSQL | User data | TCP/TLS | Service account | DMZ → Internal |

### STRIDE Threat Register

| ID | Component/Flow | Threat Type | Threat Description | Likelihood | Impact | Risk | Existing Control | Gap |
|----|---------------|-------------|-------------------|------------|--------|------|-----------------|-----|
| T-001 | User login | Spoofing | Credential stuffing, brute force | High | High | S1 | Rate limiting | No MFA |
| T-002 | API to DB | Tampering | SQL injection | Medium | Critical | S1 | Parameterized queries | None verified |
| T-003 | File upload | Elevation | RCE via malicious upload | Medium | Critical | S1 | Extension check | No content validation |

### Attack Trees (Top 3 Threats)

\```
Goal: Account Takeover
├── OR: Credential Theft
│   ├── AND: Phishing + No MFA
│   ├── AND: Credential Stuffing + Weak Rate Limiting
│   └── AND: Session Hijack + Missing Secure Cookie Flags
├── OR: Authorization Bypass
│   ├── IDOR on /api/users/:id
│   └── JWT algorithm confusion
└── OR: Password Reset Abuse
    └── AND: Predictable token + No rate limit on reset
\```

### Testing Priority Map

| Priority | Threat | Assessment Skill | Test Scenario |
|----------|--------|-----------------|---------------|
| 1 | T-001: Credential stuffing | api-security-review | Test rate limiting on /login |
| 2 | T-002: SQL injection | secure-code-review + exploit-validation | Review query construction, test payloads |
| 3 | T-003: Malicious file upload | exploit-validation | Upload polyglot, test execution |

### Assumptions & Limitations
[Documented assumptions made during modeling]

References

• references/severity-matrix.md — Risk rating alignment
• references/web-common-risks.md — Threat-to-vulnerability mapping
• references/authz-and-authn-checklist.md — Authentication threat patterns