apidash/doc/security/STRIDE_THREAT_MODEL.md

STRIDE Threat Model for API Dash

Document Version: 1.0
Date: December 2025
Project: API Dash - Open Source Cross-Platform API Client
Classification: Public

---

Executive Summary

This document presents a comprehensive threat model for API Dash using the STRIDE (Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, Elevation of Privilege) framework. API Dash is a cross-platform API testing client built with Flutter that allows users to create, test, and manage API requests locally on their devices.

Key Risk Areas Identified:

1. High Priority: Information disclosure of sensitive API credentials and tokens
2. High Priority: Tampering with stored API requests and collections
3. Medium Priority: Local data security and encryption
4. Medium Priority: Code generation security vulnerabilities

---

1. System Overview

1.1 Architecture Context

API Dash is a Flutter-based desktop and mobile application that:

• Runs locally on user devices (Windows, macOS, Linux, iOS, iPad)
• Stores data locally using Hive database
• Does not require backend servers or cloud services
• Supports multiple API types: HTTP, GraphQL, SSE/Streaming, AI
• Generates integration code in 35+ languages/libraries
• Imports collections from Postman, cURL, Insomnia, OpenAPI, HAR formats
• Integrates with AI models via Ollama for API testing assistance

1.2 Key Components

┌──────────────────────────────────────────────────────────┐
│                   User Interface Layer                    │
│  (Flutter Widgets, Screens, Request/Response Viewers)    │
└──────────────────────────────────────────────────────────┘
                            │
┌──────────────────────────────────────────────────────────┐
│                  Application Logic Layer                  │
│   (Providers, Services, Models, State Management)        │
└──────────────────────────────────────────────────────────┘
                            │
┌─────────────┬──────────────┬──────────────┬──────────────┐
│   Network   │  Local Data  │   Code Gen   │  AI/Ollama   │
│   Layer     │   Storage    │   Templates  │  Integration │
│  (HTTP/API) │   (Hive)     │              │              │
└─────────────┴──────────────┴──────────────┴──────────────┘
                            │
┌──────────────────────────────────────────────────────────┐
│                    Operating System                       │
│        (File System, Network Stack, Keychain)            │
└──────────────────────────────────────────────────────────┘

1.3 Data Flow

1. User Input → API Request Configuration (URL, headers, body, authentication)
2. Local Storage → Hive database stores requests, collections, history, settings
3. Network Request → Direct HTTP calls to target APIs
4. Response Handling → Display/store API responses (including multimedia)
5. Code Generation → Transform requests into code snippets
6. Export/Import → HAR, Postman, Insomnia, OpenAPI formats
7. AI Integration → Send prompts to local Ollama instance for assistance

1.4 Trust Boundaries

• User Device Boundary: All data resides locally on user's device
• Network Boundary: Outbound connections to user-specified APIs
• File System Boundary: Local storage for collections, history, settings
• Process Boundary: Flutter app process with OS-level isolation
• AI Service Boundary: Optional external connection to local Ollama instance

---

2. STRIDE Threat Analysis

2.1 Spoofing Identity (S)

Threat S.1: Impersonation of API Endpoints

Description: Attacker could intercept or redirect API requests to malicious endpoints through DNS poisoning, man-in-the-middle attacks, or malicious URL manipulation.

Attack Vectors:

• DNS hijacking redirecting legitimate API domains to attacker-controlled servers
• Modified hosts file on compromised systems
• Malicious proxy configuration
• SSL/TLS stripping attacks

Impact: HIGH

• Credential theft if authentication tokens are sent to malicious endpoints
• Sensitive data exfiltration
• Malware delivery through malicious responses

Affected Components:

• Network layer (HTTP client in packages/better_networking)
• Request execution services
• SSL/TLS verification

Existing Mitigations:

• Flutter's HTTP client uses system SSL/TLS certificates
• HTTPS validation by underlying platform (iOS/Android/Desktop)

Recommended Additional Mitigations:

1. Implement certificate pinning for known critical APIs (optional feature)
2. Add visual indicators for HTTPS vs HTTP connections
3. Warn users when making requests to non-HTTPS endpoints
4. Log and display certificate information for transparency
5. Implement HSTS (HTTP Strict Transport Security) awareness
6. Add option to verify server certificates explicitly

Priority: HIGH
Effort: MEDIUM

---

Threat S.2: Spoofing of Imported Collections

Description: Malicious actors could distribute modified collection files (HAR, Postman, etc.) that contain malicious URLs or scripts.

Attack Vectors:

• Social engineering users to import malicious collections
• Compromised collection sharing platforms
• Man-in-the-middle during collection download

Impact: MEDIUM

• Unintended API requests to malicious endpoints
• Potential code injection in collection metadata
• Credential leakage if malicious URLs are designed to capture tokens

Affected Components:

• Import services (lib/importer, packages/postman, packages/har)
• Collection parser modules
• File selector dialogs

Existing Mitigations:

• User explicitly chooses files to import
• No automatic execution of imported requests

Recommended Additional Mitigations:

1. Validate and sanitize imported collection data
2. Display security warnings before importing from untrusted sources
3. Implement collection integrity verification (checksums/signatures)
4. Sandbox imported scripts and prevent auto-execution
5. Add "safe mode" import that strips potentially dangerous elements
6. Provide collection preview before full import

Priority: MEDIUM
Effort: MEDIUM

---

Threat S.3: AI/LLM Model Impersonation

Description: If using external AI services (Ollama), attacker could impersonate the AI endpoint or model.

Attack Vectors:

• Malicious Ollama server running on localhost
• Modified Ollama client configuration
• DNS redirection of AI service endpoints

Impact: LOW-MEDIUM

• Malicious code suggestions or API configurations
• Information disclosure to unauthorized AI services
• Manipulation of AI-generated test scenarios

Affected Components:

• AI integration services (lib/dashbot, packages/genai)
• Ollama client integration
• Model management system

Existing Mitigations:

• Ollama typically runs locally
• User controls AI service endpoints

Recommended Additional Mitigations:

1. Verify Ollama service authenticity before sending data
2. Allow users to review/approve AI-generated requests before execution
3. Display clear indicators of AI service provider
4. Implement option to disable AI features entirely
5. Add warnings about sensitive data in AI prompts

Priority: LOW
Effort: LOW

---

2.2 Tampering (T)

Threat T.1: Local Data Store Tampering

Description: Attacker with file system access could modify Hive database files containing API requests, credentials, and collections.

Attack Vectors:

• Direct file system access on compromised devices
• Malware with file modification capabilities
• Physical access to unlocked device
• Backup/restore attacks with modified data files

Impact: HIGH

• Injection of malicious API requests
• Modification of stored credentials
• Alteration of request history for covering tracks
• Insertion of malicious code in templates

Affected Components:

• Hive database storage (lib/services/hive_services.dart)
• Data persistence layer
• Settings and preferences storage
• File system access layers

Existing Mitigations:

• OS-level file permissions protect data files
• Flutter app runs in sandboxed environment on mobile

Recommended Additional Mitigations:

1. Implement integrity verification for Hive database files (checksums/HMAC)
2. Encrypt sensitive data at rest (credentials, tokens, secrets)
3. Add tamper detection mechanisms with user alerts
4. Implement secure backup/restore with integrity checks
5. Use OS keychain/credential manager for sensitive credentials
6. Add file-level encryption for workspace directories
7. Implement audit logging for data modifications

Priority: HIGH
Effort: HIGH

---

Threat T.2: Request Parameter Injection

Description: Malicious input in request fields could lead to injection attacks (SQL injection, command injection) on target APIs.

Attack Vectors:

• Crafted input in URL, headers, body, query parameters
• Injection through environment variables
• Malicious code in code generation templates

Impact: MEDIUM

• Compromise of target API systems
• Unintended API behavior
• Security testing tool misuse for malicious purposes

Affected Components:

• Request editor UI components
• Parameter handling logic
• Code generation templates
• Environment variable substitution

Existing Mitigations:

• Application doesn't execute requests automatically
• User explicitly triggers API calls

Recommended Additional Mitigations:

1. Input validation and sanitization warnings
2. Display security notices about injection risks
3. Provide "safe mode" with automatic encoding
4. Add syntax validation for common injection patterns
5. Implement request review/approval workflow
6. Include security testing guidelines in documentation

Priority: MEDIUM
Effort: LOW

---

Threat T.3: Code Generation Template Tampering

Description: Modification of code generation templates could inject malicious code into generated snippets.

Attack Vectors:

• Direct modification of template files on disk
• Malicious template imports/extensions
• Exploiting template engine vulnerabilities (Jinja)

Impact: HIGH

• Malicious code in generated snippets used in production
• Supply chain attacks if developers share generated code
• Backdoors in generated authentication code

Affected Components:

• Code generation system (lib/codegen, lib/templates)
• Template engine (Jinja)
• Template storage and loading mechanisms

Existing Mitigations:

• Templates are part of application bundle
• User doesn't typically modify templates directly

Recommended Additional Mitigations:

1. Sign and verify integrity of template files
2. Implement template sandboxing
3. Validate generated code against security patterns
4. Add warnings for potentially dangerous code patterns
5. Provide code generation audit trail
6. Lock down template directory with strict permissions

Priority: MEDIUM
Effort: MEDIUM

---

Threat T.4: Exported Collection Tampering

Description: Exported HAR/collection files could be modified by attackers and re-imported.

Attack Vectors:

• Intercepting exported files during sharing
• Malicious modification of version-controlled collections
• Man-in-the-middle during file transfer

Impact: MEDIUM

• Re-import of tampered collections
• Distribution of malicious API configurations
• Credential theft through modified requests

Affected Components:

• Export functionality
• File system operations
• Import validation

Existing Mitigations:

• User controls export/import workflow

Recommended Additional Mitigations:

1. Digital signatures for exported collections
2. Integrity verification during import
3. Version tracking with change detection
4. Encrypted exports for sensitive collections
5. Warning on import if signatures don't match

Priority: MEDIUM
Effort: MEDIUM

---

2.3 Repudiation (R)

Threat R.1: No Audit Trail for API Requests

Description: Users can deny making specific API requests as there's no cryptographically secure audit trail.

Attack Vectors:

• User makes malicious API requests and denies responsibility
• Deletion or modification of request history
• Lack of non-repudiation mechanisms

Impact: MEDIUM

• Inability to prove which user made specific requests
• Compliance issues in regulated environments
• Difficulty in forensic investigations

Affected Components:

• History service (lib/services/history_service.dart)
• Request execution logic
• Logging mechanisms

Existing Mitigations:

• Request history is stored locally
• Timestamps on request history

Recommended Additional Mitigations:

1. Implement secure audit logging with integrity protection
2. Add optional cryptographic signing of requests (timestamp + hash)
3. Provide export of audit logs for compliance
4. Implement write-once logging to prevent deletion
5. Add user activity tracking for sensitive operations
6. Optional integration with SIEM systems for enterprises

Priority: LOW-MEDIUM
Effort: MEDIUM

---

Threat R.2: History Manipulation

Description: Users could manipulate or delete request history to hide malicious activities.

Attack Vectors:

• Direct deletion of history entries through UI
• Modification of history database files
• Clearing history to remove evidence

Impact: MEDIUM

• Loss of audit trail
• Inability to track malicious activities
• Compliance violations

Affected Components:

• History service and providers
• Hive database for history storage
• History UI components

Existing Mitigations:

• History auto-clear can be configured
• History is stored persistently

Recommended Additional Mitigations:

1. Implement append-only history log option
2. Add protected history mode requiring authentication to delete
3. Create tamper-evident logging mechanisms
4. Export history to external secure storage
5. Add retention policies with enforcement
6. Implement history backup before deletion

Priority: LOW
Effort: MEDIUM

---

2.4 Information Disclosure (I)

Threat I.1: Exposure of API Credentials in Local Storage

Description: API keys, tokens, passwords stored in Hive database are vulnerable to disclosure.

Attack Vectors:

• Malware reading application data files
• Physical access to unlocked device
• Backup files containing unencrypted credentials
• Memory dumps revealing credentials
• Insecure file permissions on workspace folders

Impact: CRITICAL

• Unauthorized access to user's APIs and services
• Data breaches on connected services
• Financial losses from API abuse
• Compromise of production systems

Affected Components:

• Request models storing auth credentials
• Environment variable storage (secrets)
• Settings containing API configurations
• Hive database files
• Memory storage of sensitive data

Existing Mitigations:

• OS-level file permissions
• Application sandboxing on mobile platforms

Recommended Additional Mitigations:

1. CRITICAL: Implement encryption at rest for all credentials
2. Use OS credential storage (Keychain on macOS/iOS, Credential Manager on Windows, Secret Service on Linux)
3. Encrypt Hive database with user-provided passphrase
4. Clear sensitive data from memory after use
5. Implement secure memory handling for credentials
6. Add option for "session-only" credentials (not persisted)
7. Provide warnings about credential storage risks
8. Implement automatic credential rotation reminders
9. Add data classification labels (sensitive/non-sensitive)

Priority: CRITICAL
Effort: HIGH

---

Threat I.2: Response Data Containing Sensitive Information

Description: API responses may contain PII, secrets, or sensitive business data stored locally without encryption.

Attack Vectors:

• Unauthorized access to history/response cache
• Malware scanning local databases
• Accidental sharing of workspace folders
• Forensic data recovery from deleted files

Impact: HIGH

• PII exposure leading to privacy violations
• Exposure of business-critical information
• Regulatory compliance violations (GDPR, CCPA)
• Reputational damage

Affected Components:

• Response storage in history
• Request/response caching
• Downloaded response files
• Screenshot/export features

Existing Mitigations:

• Data stored locally only
• User controls data retention

Recommended Additional Mitigations:

1. Encrypt stored responses containing sensitive data
2. Implement data retention policies with auto-deletion
3. Add response data classification options
4. Provide "incognito mode" that doesn't save responses
5. Implement response content redaction features
6. Add warnings when saving responses with detected sensitive patterns
7. Secure deletion (overwrite) when removing history
8. Add option to exclude response bodies from history

Priority: HIGH
Effort: MEDIUM

---

Threat I.3: Logging Sensitive Information

Description: Debug logs, error messages, or analytics could inadvertently expose sensitive data.

Attack Vectors:

• Verbose logging including credentials in URLs
• Stack traces containing sensitive data
• Crash reports sent with sensitive context
• Console logs on shared/public displays

Impact: MEDIUM

• Credential disclosure through logs
• Exposure of API internal details
• Data leakage through error messages

Affected Components:

• Logging infrastructure
• Error handling and reporting
• Debug mode operations
• Crash analytics

Existing Mitigations:

• Production builds have minimal logging

Recommended Additional Mitigations:

1. Implement credential redaction in all logs
2. Add sensitive data detection and masking
3. Create separate log levels with security considerations
4. Disable verbose logging in production builds
5. Sanitize error messages before display
6. Review all log statements for sensitive data exposure
7. Add opt-in for detailed logging with warnings

Priority: MEDIUM
Effort: LOW

---

Threat I.4: Screenshot/Screen Recording Exposure

Description: Sensitive data visible in UI could be captured through screenshots, screen recordings, or shoulder surfing.

Attack Vectors:

• Screen sharing during video calls
• Screenshot/screen recording malware
• Shoulder surfing in public spaces
• Screen capture utilities

Impact: MEDIUM

• Credential exposure through visual capture
• API key disclosure in UI
• Sensitive response data visible to unauthorized parties

Affected Components:

• All UI components displaying sensitive data
• Request/response viewers
• Environment variable editors

Existing Mitigations:

• None specific

Recommended Additional Mitigations:

1. Implement "privacy mode" that masks sensitive fields
2. Add screenshot blocking for sensitive screens (mobile)
3. Provide credential masking in UI (show/hide toggle)
4. Add visual indicators for sensitive data display
5. Implement blur overlay for sensitive content when app is backgrounded
6. Add user education about screenshot risks

Priority: MEDIUM
Effort: MEDIUM

---

Threat I.5: Clipboard Data Leakage

Description: Sensitive data copied to clipboard could be accessed by malicious applications.

Attack Vectors:

• Clipboard monitoring malware
• Other applications reading clipboard
• Clipboard history features in OS
• Cloud clipboard sync exposing credentials

Impact: MEDIUM

• Credential theft from clipboard
• Token/key exposure
• Sensitive response data leakage

Affected Components:

• Copy/paste functionality
• Code generation copy features
• Response data copying

Existing Mitigations:

• Standard OS clipboard protections

Recommended Additional Mitigations:

1. Implement auto-clear clipboard after timeout
2. Add warnings when copying sensitive data
3. Provide option to disable clipboard for sensitive fields
4. Use secure clipboard APIs where available
5. Avoid copying credentials to clipboard automatically
6. Add notification when sensitive data is copied

Priority: LOW-MEDIUM
Effort: LOW

---

Threat I.6: Exported Collection Data Exposure

Description: Exported HAR, Postman, or other collection files may contain sensitive credentials and data.

Attack Vectors:

• Unencrypted export files
• Accidental sharing of exports with credentials
• Version control commits with sensitive exports
• Cloud storage of unprotected exports

Impact: HIGH

• Credential exposure through shared files
• API configuration disclosure
• Secret tokens in version control history

Affected Components:

• Export functionality
• Collection serialization
• File save operations

Existing Mitigations:

• User controls export process

Recommended Additional Mitigations:

1. Warn users before exporting with credentials
2. Provide option to exclude sensitive data from exports
3. Encrypt exported files with password protection
4. Add credential redaction option for exports
5. Include security warnings in export dialog
6. Implement "safe export" mode for sharing
7. Add .gitignore recommendations in documentation

Priority: HIGH
Effort: MEDIUM

---

2.5 Denial of Service (DoS)

Threat D.1: Local Resource Exhaustion

Description: Malicious or poorly configured requests could exhaust local system resources.

Attack Vectors:

• Very large response bodies consuming disk space
• Infinite loops in request sequences
• Memory exhaustion from large collections
• CPU exhaustion from complex code generation

Impact: MEDIUM

• Application crashes or freezes
• System instability
• Data loss from unexpected terminations
• Poor user experience

Affected Components:

• Response handling and storage
• Collection management
• Code generation engine
• Memory management

Existing Mitigations:

• Flutter framework memory management
• OS-level resource limits

Recommended Additional Mitigations:

1. Implement response size limits with warnings
2. Add timeout mechanisms for long-running operations
3. Implement streaming for large responses
4. Add resource usage monitoring and alerts
5. Limit collection size and request count
6. Implement graceful degradation
7. Add progress indicators for resource-intensive operations
8. Implement pagination for large data sets

Priority: MEDIUM
Effort: MEDIUM

---

Threat D.2: Malicious Response Processing

Description: Crafted API responses could trigger resource exhaustion in parsing/rendering.

Attack Vectors:

• Extremely large JSON/XML responses
• Deeply nested data structures causing parser issues
• Malformed multimedia content causing decoder crashes
• Billion laughs attack in XML responses

Impact: MEDIUM

• Application crash or hang
• UI freezing
• Data corruption

Affected Components:

• Response parsers
• JSON/XML/YAML viewers
• Multimedia rendering components
• Syntax highlighters

Existing Mitigations:

• Flutter framework handles most edge cases

Recommended Additional Mitigations:

1. Implement response size validation before processing
2. Add depth limits for nested data structures
3. Implement timeout for parsing operations
4. Use streaming parsers for large responses
5. Add malformed response detection and handling
6. Implement graceful fallback for unsupported formats
7. Add response complexity analysis
8. Sandbox multimedia rendering

Priority: MEDIUM
Effort: MEDIUM

---

Threat D.3: File System DoS

Description: Continuous operations could fill up disk space or exceed inode limits.

Attack Vectors:

• Excessive history retention
• Large response body downloads
• Uncontrolled log file growth
• Multiple workspace folders with duplicated data

Impact: LOW-MEDIUM

• System-wide storage exhaustion
• Application malfunction
• Data loss from full disk

Affected Components:

• History storage
• Download functionality
• Workspace management
• Logging systems

Existing Mitigations:

• History auto-clear feature
• User controls downloads

Recommended Additional Mitigations:

1. Implement disk space monitoring
2. Add storage quota management
3. Automatic cleanup of old data
4. Warn users before large downloads
5. Implement compression for stored data
6. Add storage usage dashboard
7. Automatic log rotation

Priority: LOW
Effort: LOW

---

2.6 Elevation of Privilege (E)

Threat E.1: Code Injection via Template Engine

Description: Template engine vulnerabilities could allow code execution through crafted templates.

Attack Vectors:

• Server-Side Template Injection (SSTI) in Jinja templates
• Malicious template syntax
• Exploiting template engine vulnerabilities
• Unsafe template rendering context

Impact: HIGH

• Arbitrary code execution on user's device
• File system access beyond application scope
• Privilege escalation to user's account level

Affected Components:

• Jinja template engine (jinja: ^0.6.1)
• Code generation system
• Template rendering context

Existing Mitigations:

• Templates are bundled with application
• No user-editable templates by default

Recommended Additional Mitigations:

1. Keep Jinja library updated to latest version
2. Implement template sandboxing with restricted context
3. Validate and sanitize template inputs
4. Disable dangerous template functions
5. Code review all template files
6. Implement Content Security Policy for templates
7. Add template execution monitoring

Priority: HIGH
Effort: MEDIUM

---

Threat E.2: Unsafe Deserialization

Description: Deserializing untrusted data from imports or database could lead to code execution.

Attack Vectors:

• Malicious HAR file imports
• Crafted Postman/Insomnia collections
• Modified Hive database files
• Exploiting serialization vulnerabilities in Dart

Impact: HIGH

• Remote code execution
• Privilege escalation
• System compromise

Affected Components:

• Import parsers
• Hive database deserialization
• JSON deserialization
• Collection parsers

Existing Mitigations:

• Dart's type safety
• Structured data parsing

Recommended Additional Mitigations:

1. Implement strict input validation on all imports
2. Use safe deserialization libraries
3. Validate object types after deserialization
4. Implement schema validation for imports
5. Sandbox deserialization operations
6. Add integrity checks before deserialization
7. Limit deserializable classes to known types

Priority: HIGH
Effort: MEDIUM

---

Threat E.3: Dependency Vulnerabilities

Description: Vulnerable third-party packages could introduce privilege escalation vectors.

Attack Vectors:

• Known CVEs in dependencies
• Supply chain attacks on packages
• Malicious package updates
• Transitive dependency vulnerabilities

Impact: VARIES (LOW to CRITICAL)

• Arbitrary code execution
• Data theft
• Privilege escalation

Affected Components:

• All third-party dependencies (45+ packages)
• Package management (pubspec.yaml)
• Build system

Existing Mitigations:

• Dependabot alerts (if enabled)
• Regular dependency updates

Recommended Additional Mitigations:

1. Enable GitHub Dependabot alerts
2. Implement automated dependency scanning in CI/CD
3. Regular security audits of dependencies
4. Pin dependency versions
5. Review dependencies before major updates
6. Use only well-maintained packages
7. Implement Software Bill of Materials (SBOM)
8. Consider dependency license compliance
9. Regular vulnerability scanning with tools like dart pub outdated

Priority: HIGH
Effort: LOW-MEDIUM

---

Threat E.4: Native Code Vulnerabilities

Description: Platform-specific native code or FFI could have vulnerabilities leading to privilege escalation.

Attack Vectors:

• Buffer overflows in native plugins
• Memory corruption in FFI boundaries
• Platform-specific vulnerabilities
• Native library vulnerabilities (e.g., libcurl)

Impact: HIGH

• System-level access
• Sandbox escape
• Device compromise

Affected Components:

• Platform channels
• Native plugins (window_manager, file_selector, etc.)
• FFI implementations
• Native media players (fvp, just_audio)

Existing Mitigations:

• Flutter's sandboxing
• Platform security features

Recommended Additional Mitigations:

1. Audit all native code integrations
2. Use memory-safe languages where possible
3. Implement bounds checking in FFI code
4. Regular security updates for native components
5. Minimize use of native code
6. Implement runtime protections (ASLR, DEP)
7. Security testing of platform channels

Priority: MEDIUM
Effort: HIGH

---

Threat E.5: JavaScript Execution Context

Description: flutter_js package enables JavaScript execution which could be exploited.

Attack Vectors:

• Malicious JavaScript in imported collections
• XSS-like attacks through JS evaluation
• Arbitrary code execution via JS engine
• Exploiting JS engine vulnerabilities

Impact: HIGH

• Arbitrary code execution
• Sandbox escape
• Access to sensitive application data

Affected Components:

• flutter_js: ^0.8.5 package
• JavaScript runtime integration
• Script execution contexts

Existing Mitigations:

• Controlled JS execution context

Recommended Additional Mitigations:

1. Implement strict JavaScript sandboxing
2. Validate and sanitize all JS code before execution
3. Limit JS API access to safe operations only
4. Add user confirmation before executing scripts
5. Implement CSP-like policies for JS execution
6. Review all JS execution paths
7. Consider removing/replacing JS execution if not essential
8. Add execution timeout and resource limits

Priority: HIGH
Effort: MEDIUM

---

3. Risk Assessment Matrix

3.1 Risk Scoring

Risks are scored based on Likelihood × Impact:

Risk Level	Score Range	Color Code
Critical	20-25	🔴 Red
High	15-19	🟠 Orange
Medium	8-14	🟡 Yellow
Low	1-7	🟢 Green

Likelihood Scale: 1 (Very Unlikely) to 5 (Very Likely)
Impact Scale: 1 (Negligible) to 5 (Critical)

3.2 Risk Summary Table

Threat ID	Category	Threat Name	Likelihood	Impact	Risk Score	Priority
I.1	Information Disclosure	API Credentials Exposure	4	5	20	🔴 CRITICAL
E.3	Elevation of Privilege	Dependency Vulnerabilities	4	4	16	🔴 HIGH
T.1	Tampering	Local Data Store Tampering	3	5	15	🔴 HIGH
I.6	Information Disclosure	Exported Collection Exposure	4	4	16	🔴 HIGH
E.1	Elevation of Privilege	Template Engine Code Injection	2	5	10	🟠 HIGH
E.2	Elevation of Privilege	Unsafe Deserialization	2	5	10	🟠 HIGH
E.5	Elevation of Privilege	JavaScript Execution	2	5	10	🟠 HIGH
S.1	Spoofing	API Endpoint Impersonation	3	4	12	🟠 HIGH
I.2	Information Disclosure	Sensitive Response Data	4	4	16	🟠 HIGH
T.3	Tampering	Code Generation Templates	2	4	8	🟡 MEDIUM
S.2	Spoofing	Imported Collection Spoofing	3	3	9	🟡 MEDIUM
T.2	Tampering	Request Parameter Injection	3	3	9	🟡 MEDIUM
T.4	Tampering	Exported Collection Tampering	2	3	6	🟡 MEDIUM
I.3	Information Disclosure	Logging Sensitive Data	3	3	9	🟡 MEDIUM
I.4	Information Disclosure	Screenshot Exposure	3	3	9	🟡 MEDIUM
D.1	Denial of Service	Resource Exhaustion	3	3	9	🟡 MEDIUM
D.2	Denial of Service	Malicious Response Processing	2	3	6	🟡 MEDIUM
E.4	Elevation of Privilege	Native Code Vulnerabilities	2	4	8	🟡 MEDIUM
R.1	Repudiation	No Audit Trail	2	3	6	🟡 MEDIUM
R.2	Repudiation	History Manipulation	2	3	6	🟡 MEDIUM
I.5	Information Disclosure	Clipboard Leakage	2	2	4	🟢 LOW
S.3	Spoofing	AI Model Impersonation	2	2	4	🟢 LOW
D.3	Denial of Service	File System DoS	2	2	4	🟢 LOW

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4. Recommended Security Controls

4.1 Immediate Actions (Critical Priority)

1. Implement Secure Credential Storage

   • Migrate from plain-text Hive storage to OS credential managers
   • Implement encryption at rest for all sensitive data
   • Use Keychain (macOS/iOS), Credential Manager (Windows), Secret Service (Linux)
   • Effort: High | Impact: Critical reduction in I.1

2. Enable Dependency Scanning

   • Activate GitHub Dependabot
   • Implement automated security scanning in CI/CD
   • Regular dependency updates and security patches
   • Effort: Low | Impact: Addresses E.3

3. Implement Data Integrity Verification

   • Add HMAC/checksums for Hive database files
   • Tamper detection for local storage
   • Integrity verification on app startup
   • Effort: Medium | Impact: Addresses T.1

4. Secure Export Functionality

   • Add warnings before exporting with credentials
   • Implement credential redaction options
   • Add export encryption options
   • Effort: Medium | Impact: Addresses I.6

4.2 Short-term Actions (High Priority)

5. Enhanced HTTPS/TLS Validation

   • Add certificate information display
   • Implement warnings for non-HTTPS endpoints
   • Optional certificate pinning
   • Effort: Medium | Impact: Addresses S.1

6. JavaScript Sandboxing

   • Implement strict sandbox for flutter_js
   • Add user confirmation for script execution
   • Limit JS API access
   • Effort: Medium | Impact: Addresses E.5

7. Template Security Hardening

   • Audit and sandbox Jinja templates
   • Implement template integrity verification
   • Regular security review of templates
   • Effort: Medium | Impact: Addresses E.1

8. Import Validation

   • Schema validation for all imports
   • Sanitization of imported data
   • Security warnings for untrusted imports
   • Effort: Medium | Impact: Addresses E.2, S.2

4.3 Medium-term Actions

9. Response Data Protection

   • Implement encryption for stored responses
   • Add "incognito mode" for sensitive testing
   • Data retention policies with auto-deletion
   • Effort: Medium | Impact: Addresses I.2

10. Privacy Mode Features

    • Credential masking in UI
    • Screenshot blocking on sensitive screens
    • Blur overlay when app backgrounded
    • Effort: Medium | Impact: Addresses I.4

11. Audit and Logging

    • Implement secure audit trail
    • Credential redaction in logs
    • Optional append-only history mode
    • Effort: Medium | Impact: Addresses R.1, R.2, I.3

12. Resource Management

    • Response size limits
    • Memory usage monitoring
    • Graceful handling of large responses
    • Effort: Medium | Impact: Addresses D.1, D.2

4.4 Long-term Actions

13. Native Code Security Review

    • Audit all platform channel implementations
    • Security testing of FFI boundaries
    • Minimize native code dependencies
    • Effort: High | Impact: Addresses E.4

14. Comprehensive Security Testing

    • Automated security testing in CI/CD
    • Regular penetration testing
    • Fuzzing for parsers and importers
    • Effort: High | Impact: Addresses multiple threats

15. Security Documentation

    • Developer security guidelines
    • User security best practices
    • Incident response procedures
    • Effort: Low | Impact: General security awareness

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5. Assumptions and Constraints

5.1 Security Assumptions

1. Local-First Design: API Dash is primarily a local application without cloud backend
2. User Trust: Users are trusted to configure and use the application appropriately
3. OS Security: Underlying OS provides basic security (sandboxing, file permissions)
4. No Authentication: Application itself has no authentication mechanism
5. Open Source: All code is publicly visible for security review
6. No Remote Control: No remote management or control features

5.2 Out of Scope

The following are explicitly out of scope for this threat model:

1. Physical security of user devices
2. User's network security and infrastructure
3. Security of target APIs being tested
4. Social engineering attacks against users
5. Insider threats from legitimate users
6. Legal/compliance aspects beyond technical controls
7. Third-party service security (Ollama, external APIs)

5.3 Constraints

1. Open Source: Cannot implement security through obscurity
2. Cross-Platform: Solutions must work across all supported platforms
3. User Experience: Security controls must not significantly degrade UX
4. Backward Compatibility: Must maintain data compatibility with existing users
5. Resource Limitations: Open source project with limited dedicated security resources
6. Performance: Security controls must not significantly impact performance

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6. Security Architecture Recommendations

6.1 Secure-by-Default Configuration

// Example: Default secure settings
class SecurityDefaults {
  static const bool httpsOnly = false; // Warning shown for HTTP
  static const bool encryptLocalData = true; // Encrypt sensitive data
  static const bool useOSKeychain = true; // Use OS credential storage
  static const bool clearClipboardAfter = 30; // seconds
  static const int maxResponseSize = 100; // MB
  static const int historyRetentionDays = 30;
  static const bool incognitoMode = false; // Don't save history
  static const bool maskCredentialsInUI = true;
}

6.2 Defense in Depth Strategy

┌─────────────────────────────────────────────────────────┐
│ Layer 1: Input Validation & Sanitization               │
├─────────────────────────────────────────────────────────┤
│ Layer 2: Encryption (Data at Rest & In Transit)        │
├─────────────────────────────────────────────────────────┤
│ Layer 3: Access Control & Authentication               │
├─────────────────────────────────────────────────────────┤
│ Layer 4: Audit Logging & Monitoring                    │
├─────────────────────────────────────────────────────────┤
│ Layer 5: Security Updates & Patch Management           │
└─────────────────────────────────────────────────────────┘

6.3 Secure Development Lifecycle Integration

1. Design Phase: Security requirements and threat modeling
2. Development: Secure coding guidelines and code reviews
3. Testing: Security testing, SAST/DAST, dependency scanning
4. Release: Security validation, signed releases
5. Maintenance: Security patches, vulnerability management

---

7. Security Testing Recommendations

7.1 Recommended Security Tests

1. Static Analysis

   • SAST tools for Dart code
   • Dependency vulnerability scanning
   • Code quality and security linters

2. Dynamic Analysis

   • Fuzzing for parsers (HAR, Postman, JSON, XML)
   • Memory leak detection
   • Runtime security monitoring

3. Manual Testing

   • Penetration testing
   • Code review of security-critical components
   • Template injection testing
   • Deserialization vulnerability testing

4. Compliance Testing

   • OWASP Mobile Top 10 validation
   • Privacy compliance (GDPR, CCPA)
   • Accessibility security testing

7.2 Security Test Cases

Test Case: Credential Storage Security
- Verify credentials are encrypted at rest
- Verify OS keychain integration
- Verify memory is cleared after use
- Test unauthorized access to storage

Test Case: Import Security
- Test malicious HAR file import
- Test oversized collection imports
- Test script injection in collections
- Verify validation of imported data

Test Case: Export Security
- Verify credential warning on export
- Test export encryption
- Verify redaction options
- Test integrity of exported files

---

8. Incident Response Integration

This threat model should be used in conjunction with the Incident Response Plan (IRP) for:

1. Vulnerability Prioritization: Use risk scores to prioritize incident response
2. Impact Assessment: Threat descriptions inform incident severity
3. Containment Strategies: Mitigations guide incident containment
4. Recovery Planning: Recommended controls aid in recovery

---

9. Compliance and Regulatory Considerations

9.1 Applicable Regulations

While API Dash is a developer tool, it may be subject to:

1. GDPR (EU): If processing EU citizen data
2. CCPA (California): If processing California resident data
3. SOC 2: For enterprise adoption
4. ISO 27001: Information security management
5. OWASP: Mobile and API security guidelines

9.2 Security Requirements

Requirement	STRIDE Coverage	Status
Data Encryption	I.1, I.2, I.6	Partially Implemented
Access Control	E.1-E.5	Limited
Audit Logging	R.1, R.2	Partially Implemented
Input Validation	T.2, S.2	Partially Implemented
Secure Communications	S.1	Partially Implemented
Incident Response	All	Documented Separately

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10. Conclusion and Next Steps

10.1 Summary

API Dash has a solid foundation with local-first architecture and open-source transparency. However, several critical security improvements are needed, particularly around:

1. Credential and sensitive data encryption (CRITICAL)
2. Dependency vulnerability management (HIGH)
3. Data integrity verification (HIGH)
4. Secure export functionality (HIGH)

10.2 Recommended Action Plan

Phase 1 (0-3 months): Address critical priority items

• Implement secure credential storage
• Enable dependency scanning
• Add data integrity checks
• Secure export warnings

Phase 2 (3-6 months): Address high priority items

• HTTPS/TLS enhancements
• JavaScript sandboxing
• Template security
• Import validation

Phase 3 (6-12 months): Address medium/long-term items

• Response data protection
• Privacy features
• Comprehensive audit logging
• Security testing program

10.3 Continuous Improvement

This threat model should be:

• Reviewed quarterly for updates
• Updated when major features are added
• Revised after security incidents
• Validated through security testing
• Shared with the security community for feedback

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11. Document Control

Version	Date	Author	Changes
1.0	December 2025	Security Team	Initial STRIDE threat model

Review Schedule: Quarterly
Next Review Date: March 2026
Document Owner: API Dash Security Team
Classification: Public

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References

1. STRIDE Threat Modeling - Microsoft
2. OWASP Mobile Security Testing Guide
3. OWASP API Security Top 10
4. CWE Top 25 Most Dangerous Software Weaknesses
5. Flutter Security Best Practices
6. API Dash Repository: https://github.com/foss42/apidash
7. API Dash Security Policy: SECURITY.md

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END OF STRIDE THREAT MODEL