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stride-analysis-patterns
Apply STRIDE methodology to systematically identify threats. Use
# STRIDE Analysis Patterns
Systematic threat identification using the STRIDE methodology.
## When to Use This Skill
- 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
## Core Concepts
### 1. STRIDE Categories
```
S - Spoofing ā Authentication threats
T - Tampering ā Integrity threats
R - Repudiation ā Non-repudiation threats
I - Information ā Confidentiality threats
Disclosure
D - Denial of ā Availability threats
Service
E - Elevation of ā Authorization threats
Privilege
```
### 2. Threat Analysis Matrix
| Category | Question | Control Family |
| ------------------- | ----------------------------------------- | -------------- |
| **Spoofing** | Can attacker pretend to be someone else? | Authentication |
| **Tampering** | Can attacker modify data in transit/rest? | Integrity |
| **Repudiation** | Can attacker deny actions? | Logging/Audit |
| **Info Disclosure** | Can attacker access unauthorized data? | Encryption |
| **DoS** | Can attacker disrupt availability? | Rate limiting |
| **Elevation** | Can attacker gain higher privileges? | Authorization |
## Templates
### Template 1: STRIDE Threat Model Document
```markdown
# Threat Model: [System Name]
## 1. System Overview
### 1.1 Description
[Brief description of the system and its purpose]
### 1.2 Data Flow Diagram
```
[User] --> [Web App] --> [API Gateway] --> [Backend Services]
|
v
[Database]
```
### 1.3 Trust Boundaries
- **External Boundary**: Internet to DMZ
- **Internal Boundary**: DMZ to Internal Network
- **Data Boundary**: Application to Database
## 2. Assets
| Asset | Sensitivity | Description |
|-------|-------------|-------------|
| User Credentials | High | Authentication tokens, passwords |
| Personal Data | High | PII, financial information |
| Session Data | Medium | Active user sessions |
| Application Logs | Medium | System activity records |
| Configuration | High | System settings, secrets |
## 3. STRIDE Analysis
### 3.1 Spoofing Threats
| ID | Threat | Target | Impact | Likelihood |
|----|--------|--------|--------|------------|
| S1 | Session hijacking | User sessions | High | Medium |
| S2 | Token forgery | JWT tokens | High | Low |
| S3 | Credential stuffing | Login endpoint | High | High |
**Mitigations:**
- [ ] Implement MFA
- [ ] Use secure session management
- [ ] Implement account lockout policies
### 3.2 Tampering Threats
| ID | Threat | Target | Impact | Likelihood |
|----|--------|--------|--------|------------|
| T1 | SQL injection | Database queries | Critical | Medium |
| T2 | Parameter manipulation | API requests | High | High |
| T3 | File upload abuse | File storage | High | Medium |
**Mitigations:**
- [ ] Input validation on all endpoints
- [ ] Parameterized queries
- [ ] File type validation
### 3.3 Repudiation Threats
| ID | Threat | Target | Impact | Likelihood |
|----|--------|--------|--------|------------|
| R1 | Transaction denial | Financial ops | High | Medium |
| R2 | Access log tampering | Audit logs | Medium | Low |
| R3 | Action attribution | User actions | Medium | Medium |
**Mitigations:**
- [ ] Comprehensive audit logging
- [ ] Log integrity protection
- [ ] Digital signatures for critical actions
### 3.4 Information Disclosure Threats
| ID | Threat | Target | Impact | Likelihood |
|----|--------|--------|--------|------------|
| I1 | Data breach | User PII | Critical | Medium |
| I2 | Error message leakage | System info | Low | High |
| I3 | Insecure transmission | Network traffic | High | Medium |
**Mitigations:**
- [ ] Encryption at rest and in transit
- [ ] Sanitize error messages
- [ ] Implement TLS 1.3
### 3.5 Denial of Service Threats
| ID | Threat | Target | Impact | Likelihood |
|----|--------|--------|--------|------------|
| D1 | Resource exhaustion | API servers | High | High |
| D2 | Database overload | Database | Critical | Medium |
| D3 | Bandwidth saturation | Network | High | Medium |
**Mitigations:**
- [ ] Rate limiting
- [ ] Auto-scaling
- [ ] DDoS protection
### 3.6 Elevation of Privilege Threats
| ID | Threat | Target | Impact | Likelihood |
|----|--------|--------|--------|------------|
| E1 | IDOR vulnerabilities | User resources | High | High |
| E2 | Role manipulation | Admin access | Critical | Low |
| E3 | JWT claim tampering | Authorization | High | Medium |
**Mitigations:**
- [ ] Proper authorization checks
- [ ] Principle of least privilege
- [ ] Server-side role validation
## 4. Risk Assessment
### 4.1 Risk Matrix
```
IMPACT
Low Med High Crit
Low 1 2 3 4
L Med 2 4 6 8
I High 3 6 9 12
K Crit 4 8 12 16
```
### 4.2 Prioritized Risks
| Rank | Threat | Risk Score | Priority |
|------|--------|------------|----------|
| 1 | SQL Injection (T1) | 12 | Critical |
| 2 | IDOR (E1) | 9 | High |
| 3 | Credential Stuffing (S3) | 9 | High |
| 4 | Data Breach (I1) | 8 | High |
## 5. Recommendations
### Immediate Actions
1. Implement input validation framework
2. Add rate limiting to authentication endpoints
3. Enable comprehensive audit logging
### Short-term (30 days)
1. Deploy WAF with OWASP ruleset
2. Implement MFA for sensitive operations
3. Encrypt all PII at rest
### Long-term (90 days)
1. Security awareness training
2. Penetration testing
3. Bug bounty program
```
### Template 2: STRIDE Analysis Code
```python
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Dict, Optional
import json
class StrideCategory(Enum):
SPOOFING = "S"
TAMPERING = "T"
REPUDIATION = "R"
INFORMATION_DISCLOSURE = "I"
DENIAL_OF_SERVICE = "D"
ELEVATION_OF_PRIVILEGE = "E"
class Impact(Enum):
LOW = 1
MEDIUM = 2
HIGH = 3
CRITICAL = 4
class Likelihood(Enum):
LOW = 1
MEDIUM = 2
HIGH = 3
CRITICAL = 4
@dataclass
class Threat:
id: str
category: StrideCategory
title: str
description: str
target: str
impact: Impact
likelihood: Likelihood
mitigations: List[str] = field(default_factory=list)
status: str = "open"
@property
def risk_score(self) -> int:
return self.impact.value * self.likelihood.value
@property
def risk_level(self) -> str:
score = self.risk_score
if score >= 12:
return "Critical"
elif score >= 6:
return "High"
elif score >= 3:
return "Medium"
return "Low"
@dataclass
class Asset:
name: str
sensitivity: str
description: str
data_classification: str
@dataclass
class TrustBoundary:
name: str
description: str
from_zone: str
to_zone: str
@dataclass
class ThreatModel:
name: str
version: str
description: str
assets: List[Asset] = field(default_factory=list)
boundaries: List[TrustBoundary] = field(default_factory=list)
threats: List[Threat] = field(default_factory=list)
def add_threat(self, threat: Threat) -> None:
self.threats.append(threat)
def get_threats_by_category(self, category: StrideCategory) -> List[Threat]:
return [t for t in self.threats if t.category == category]
def get_critical_threats(self) -> List[Threat]:
return [t for t in self.threats if t.risk_level in ("Critical", "High")]
def generate_report(self) -> Dict:
"""Generate threat model report."""
return {
"summary": {
"name": self.name,
"version": self.version,
"total_threats": len(self.threats),
"critical_threats": len([t for t in self.threats if t.risk_level == "Critical"]),
"high_threats": len([t for t in self.threats if t.risk_level == "High"]),
},
"by_category": {
cat.name: len(self.get_threats_by_category(cat))
for cat in StrideCategory
},
"top_risks": [
{
"id": t.id,
"title": t.title,
"risk_score": t.risk_score,
"risk_level": t.risk_level
}
for t in sorted(self.threats, key=lambda x: x.risk_score, reverse=True)[:10]
]
}
class StrideAnalyzer:
"""Automated STRIDE analysis helper."""
STRIDE_QUESTIONS = {
StrideCategory.SPOOFING: [
"Can an attacker impersonate a legitimate user?",
"Are authentication tokens properly validated?",
"Can session identifiers be predicted or stolen?",
"Is multi-factor authentication available?",
],
StrideCategory.TAMPERING: [
"Can data be modified in transit?",
"Can data be modified at rest?",
"Are input validation controls sufficient?",
"Can an attacker manipulate application logic?",
],
StrideCategory.REPUDIATION: [
"Are all security-relevant actions logged?",
"Can logs be tampered with?",
"Is there sufficient attribution for actions?",
"Are timestamps reliable and synchronized?",
],
StrideCategory.INFORMATION_DISCLOSURE: [
"Is sensitive data encrypted at rest?",
"Is sensitive data encrypted in transit?",
"Can error messages reveal sensitive information?",
"Are access controls properly enforced?",
],
StrideCategory.DENIAL_OF_SERVICE: [
"Are rate limits implemented?",
"Can resources be exhausted by malicious input?",
"Is there protection against amplification attacks?",
"Are there single points of failure?",
],
StrideCategory.ELEVATION_OF_PRIVILEGE: [
"Are authorization checks performed consistently?",
"Can users access other users' resources?",
"Can privilege escalation occur through parameter manipulation?",
"Is the principle of least privilege followed?",
],
}
def generate_questionnaire(self, component: str) -> List[Dict]:
"""Generate STRIDE questionnaire for a component."""
questionnaire = []
for category, questions in self.STRIDE_QUESTIONS.items():
for q in questions:
questionnaire.append({
"component": component,
"category": category.name,
"question": q,
"answer": None,
"notes": ""
})
return questionnaire
def suggest_mitigations(self, category: StrideCategory) -> List[str]:
"""Suggest common mitigations for a STRIDE category."""
mitigations = {
StrideCategory.SPOOFING: [
"Implement multi-factor authentication",
"Use secure session management",
"Implement account lockout policies",
"Use cryptographically secure tokens",
"Validate authentication at every request",
],
StrideCategory.TAMPERING: [
"Implement input validation",
"Use parameterized queries",
"Apply integrity checks (HMAC, signatures)",
"Implement Content Security Policy",
"Use immutable infrastructure",
],
StrideCategory.REPUDIATION: [
"Enable comprehensive audit logging",
"Protect log integrity",
"Implement digital signatures",
"Use centralized, tamper-evident logging",
"Maintain accurate timestamps",
],
StrideCategory.INFORMATION_DISCLOSURE: [
"Encrypt data at rest and in transit",
"Implement proper access controls",
"Sanitize error messages",
"Use secure defaults",
"Implement data classification",
],
StrideCategory.DENIAL_OF_SERVICE: [
"Implement rate limiting",
"Use auto-scaling",
"Deploy DDoS protection",
"Implement circuit breakers",
"Set resource quotas",
],
StrideCategory.ELEVATION_OF_PRIVILEGE: [
"Implement proper authorization",
"Follow principle of least privilege",
"Validate permissions server-side",
"Use role-based access control",
"Implement security boundaries",
],
}
return mitigations.get(category, [])
```
### Template 3: Data Flow Diagram Analysis
```python
from dataclasses import dataclass
from typing import List, Set, Tuple
from enum import Enum
class ElementType(Enum):
EXTERNAL_ENTITY = "external"
PROCESS = "process"
DATA_STORE = "datastore"
DATA_FLOW = "dataflow"
@dataclass
class DFDElement:
id: str
name: str
type: ElementType
trust_level: int # 0 = untrusted, higher = more trusted
description: str = ""
@dataclass
class DataFlow:
id: str
name: str
source: str
destination: str
data_type: str
protocol: str
encrypted: bool = False
class DFDAnalyzer:
"""Analyze Data Flow Diagrams for STRIDE threats."""
def __init__(self):
self.elements: Dict[str, DFDElement] = {}
self.flows: List[DataFlow] = []
def add_element(self, element: DFDElement) -> None:
self.elements[element.id] = element
def add_flow(self, flow: DataFlow) -> None:
self.flows.append(flow)
def find_trust_boundary_crossings(self) -> List[Tuple[DataFlow, int]]:
"""Find data flows that cross trust boundaries."""
crossings = []
for flow in self.flows:
source = self.elements.get(flow.source)
dest = self.elements.get(flow.destination)
if source and dest and source.trust_level != dest.trust_level:
trust_diff = abs(source.trust_level - dest.trust_level)
crossings.append((flow, trust_diff))
return sorted(crossings, key=lambda x: x[1], reverse=True)
def identify_threats_per_element(self) -> Dict[str, List[StrideCategory]]:
"""Map applicable STRIDE categories to element types."""
threat_mapping = {
ElementType.EXTERNAL_ENTITY: [
StrideCategory.SPOOFING,
StrideCategory.REPUDIATION,
],
ElementType.PROCESS: [
StrideCategory.SPOOFING,
StrideCategory.TAMPERING,
StrideCategory.REPUDIATION,
StrideCategory.INFORMATION_DISCLOSURE,
StrideCategory.DENIAL_OF_SERVICE,
StrideCategory.ELEVATION_OF_PRIVILEGE,
],
ElementType.DATA_STORE: [
StrideCategory.TAMPERING,
StrideCategory.REPUDIATION,
StrideCategory.INFORMATION_DISCLOSURE,
StrideCategory.DENIAL_OF_SERVICE,
],
ElementType.DATA_FLOW: [
StrideCategory.TAMPERING,
StrideCategory.INFORMATION_DISCLOSURE,
StrideCategory.DENIAL_OF_SERVICE,
],
}
result = {}
for elem_id, elem in self.elements.items():
result[elem_id] = threat_mapping.get(elem.type, [])
return result
def analyze_unencrypted_flows(self) -> List[DataFlow]:
"""Find unencrypted data flows crossing trust boundaries."""
risky_flows = []
for flow in self.flows:
if not flow.encrypted:
source = self.elements.get(flow.source)
dest = self.elements.get(flow.destination)
if source and dest and source.trust_level != dest.trust_level:
risky_flows.append(flow)
return risky_flows
def generate_threat_enumeration(self) -> List[Dict]:
"""Generate comprehensive threat enumeration."""
threats = []
element_threats = self.identify_threats_per_element()
for elem_id, categories in element_threats.items():
elem = self.elements[elem_id]
for category in categories:
threats.append({
"element_id": elem_id,
"element_name": elem.name,
"element_type": elem.type.value,
"stride_category": category.name,
"description": f"{category.name} threat against {elem.name}",
"trust_level": elem.trust_level
})
return threats
```
### Template 4: STRIDE per Interaction
```python
from typing import List, Dict, Optional
from dataclasses import dataclass
@dataclass
class Interaction:
"""Represents an interaction between two components."""
id: str
source: str
target: str
action: str
data: str
protocol: str
class StridePerInteraction:
"""Apply STRIDE to each interaction in the system."""
INTERACTION_THREATS = {
# Source type -> Target type -> Applicable threats
("external", "process"): {
"S": "External entity spoofing identity to process",
"T": "Tampering with data sent to process",
"R": "External entity denying sending data",
"I": "Data exposure during transmission",
"D": "Flooding process with requests",
"E": "Exploiting process to gain privileges",
},
("process", "datastore"): {
"T": "Process tampering with stored data",
"R": "Process denying data modifications",
"I": "Unauthorized data access by process",
"D": "Process exhausting storage resources",
},
("process", "process"): {
"S": "Process spoofing another process",
"T": "Tampering with inter-process data",
"I": "Data leakage between processes",
"D": "One process overwhelming another",
"E": "Process gaining elevated access",
},
}
def analyze_interaction(
self,
interaction: Interaction,
source_type: str,
target_type: str
) -> List[Dict]:
"""Analyze a single interaction for STRIDE threats."""
threats = []
key = (source_type, target_type)
applicable_threats = self.INTERACTION_THREATS.get(key, {})
for stride_code, description in applicable_threats.items():
threats.append({
"interaction_id": interaction.id,
"source": interaction.source,
"target": interaction.target,
"stride_category": stride_code,
"threat_description": description,
"context": f"{interaction.action} - {interaction.data}",
})
return threats
def generate_threat_matrix(
self,
interactions: List[Interaction],
element_types: Dict[str, str]
) -> List[Dict]:
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