
data-validation
by igbuend
A repository of security related skills - like secure code review and pentesting - for Claude and other AI.
SKILL.md
name: data-validation description: Security pattern for input validation and sanitization. Use when implementing input handling, preventing injection attacks (SQL, XSS, command), ensuring data integrity, or processing data from untrusted sources. Addresses "Entity provides unexpected data" problem.
Data Validation Security Pattern
Ensures all incoming data is validated against specifications before processing, preventing injection attacks, data corruption, and unexpected behavior.
Problem Addressed
Entity provides unexpected data: Malicious or malformed input causes:
- Injection attacks (SQL, XSS, command injection)
- System crashes or unexpected behavior
- Data corruption
- Security bypasses
Core Components
| Role | Type | Responsibility |
|---|---|---|
| Entity | Entity | Sends data to system |
| Enforcer | Enforcement Point | Intercepts all incoming data |
| Validator | Decision Point | Validates data against specification |
| Specification Provider | Information Point | Manages validation rules |
| System | Entity | Processes validated data |
Data Elements
- data: Input from entity (raw)
- canonical_data: Normalized, validated form
- specification: Rules defining valid data
- type: Identifier for applicable specification
- error: Validation failure message
Validation Flow
Entity → [data] → Enforcer
Enforcer → [data] → Validator
Validator → [get_specification(type)] → Specification Provider
Specification Provider → [specification] → Validator
Validator → [validate, transform to canonical] → Validator
Validator → [canonical_data or error] → Enforcer
Enforcer → [canonical_data] → System (if valid)
→ [error] → Entity (if invalid)
- Enforcer intercepts ALL incoming data
- Validator retrieves appropriate specification
- Validator transforms to canonical form
- Validator checks against specification
- Valid: canonical data forwarded to System
- Invalid: error returned to Entity
Validation Principles
Validate Everything
- All data from uncontrolled sources
- Parameters, headers, cookies, files
- Data from APIs, databases (defense in depth)
Canonical Form
Transform data to standardized form:
- Remove/escape special characters
- Decode encoded values
- Normalize Unicode
- Parse structured data to typed objects
Benefit: System only processes data in known format.
Allowlist vs. Blocklist
- Allowlist (preferred): Define what IS allowed
- Blocklist (risky): Define what is NOT allowed
Blocklists fail against unknown attack patterns. Use allowlists.
Validate Early, Validate Often
- Validate at system boundary (earliest point)
- Re-validate near code that relies on data
- Defense in depth
Validation Types
Type Validation
- Ensure data matches expected type
- Integer, string, boolean, date, email, URL
Range/Length Validation
- Numeric bounds
- String length limits
- Array size limits
Format Validation
- Regular expressions (carefully!)
- Structural patterns
- Protocol conformance
Business Logic Validation
- Application-specific rules
- Cross-field validation
- State-dependent validation
Security Considerations
Validation ≠ Authorization
- Validation: Is this data well-formed?
- Authorization: Is entity allowed to use this data?
Both are required. Valid data doesn't mean authorized access.
Error Messages
- Don't reveal validation internals to attackers
- Log detailed errors server-side
- Return generic errors to clients
Encoding Output
Validation alone doesn't prevent all injection:
- Still encode output for context (HTML, SQL, etc.)
- Use parameterized queries
- Use context-appropriate escaping
File Uploads
Special validation needed:
- Verify content type (not just extension)
- Scan for malware
- Restrict file sizes
- Store outside web root
Structured Data (JSON, XML)
- Parse with secure parser
- Disable external entity processing (XXE)
- Validate against schema
- Limit nesting depth
Regular Expression Safety
- Avoid ReDoS-vulnerable patterns
- Limit input length before regex
- Test regex performance with malicious input
Common Validation Scenarios
| Input Type | Validations |
|---|---|
| Username | Length, allowed characters, no control chars |
| Format, length, allowlist domains (if applicable) | |
| Integer | Type, range, positive/negative |
| URL | Protocol allowlist, format, no javascript: |
| File | Extension, content-type, size, malware scan |
| JSON | Schema validation, depth limits, size limits |
Implementation Checklist
- All entry points have validation
- Canonical form transformation
- Allowlist-based rules
- Type checking
- Length/range limits
- Business rule validation
- Secure error handling
- Output encoding (separate from validation)
- File upload validation
- Structured data parsing safely
- Re-validation near sensitive operations
Related Patterns
- Authorisation (validation doesn't replace authorization)
- Selective encrypted transmission (protect data in transit)
- Log entity actions (log validation failures)
References
- Source: https://securitypatterns.distrinet-research.be/patterns/04_01_001__data_validation/
- OWASP Input Validation Cheat Sheet
- OWASP XSS Prevention Cheat Sheet
Score
Total Score
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