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igbuend

data-validation

by igbuend

A repository of security related skills - like secure code review and pentesting - for Claude and other AI.

2🍴 1📅 2026年1月24日
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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

RoleTypeResponsibility
EntityEntitySends data to system
EnforcerEnforcement PointIntercepts all incoming data
ValidatorDecision PointValidates data against specification
Specification ProviderInformation PointManages validation rules
SystemEntityProcesses 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)
  1. Enforcer intercepts ALL incoming data
  2. Validator retrieves appropriate specification
  3. Validator transforms to canonical form
  4. Validator checks against specification
  5. Valid: canonical data forwarded to System
  6. 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 TypeValidations
UsernameLength, allowed characters, no control chars
EmailFormat, length, allowlist domains (if applicable)
IntegerType, range, positive/negative
URLProtocol allowlist, format, no javascript:
FileExtension, content-type, size, malware scan
JSONSchema 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
  • Authorisation (validation doesn't replace authorization)
  • Selective encrypted transmission (protect data in transit)
  • Log entity actions (log validation failures)

References

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