TL;DR

AI APIs face unique security challenges: prompt injection, model extraction, and abuse at scale. Secure them with strong authentication, rate limiting, input validation, and output filtering. Most attacks exploit basic oversights, not sophisticated vulnerabilities.

Why it matters

Your AI API is a direct line to expensive compute resources and potentially sensitive model capabilities. Unsecured APIs get abused quickly—attackers will extract your model, run up your costs, or exploit your system for malicious purposes. Proper security protects both your organization and your users.

Authentication fundamentals

API key management

The basics matter most:

Generation:

  • Use cryptographically secure random keys (256+ bits)
  • Never use predictable patterns
  • Generate unique keys per client/application

Storage:

  • Never hardcode keys in source code
  • Use secrets management systems (Vault, AWS Secrets Manager)
  • Encrypt keys at rest
  • Rotate keys periodically

Transmission:

  • Always use HTTPS
  • Send keys in headers, not URLs
  • Never log full API keys

Beyond API keys

For higher security needs:

Method Use case Complexity
API keys Basic access control Low
OAuth 2.0 User-delegated access Medium
JWT tokens Stateless authentication Medium
Mutual TLS High-security environments High

Rate limiting strategies

Why rate limit?

Without limits, attackers can:

  • Extract your model through repeated queries
  • Run up massive compute costs
  • Deny service to legitimate users
  • Probe for vulnerabilities at scale

Implementing effective limits

Tiered approach:

Free tier:     10 requests/minute, 100/day
Basic tier:    60 requests/minute, 1000/day
Pro tier:      300 requests/minute, 10000/day
Enterprise:    Custom limits with dedicated capacity

What to limit:

  • Requests per time window
  • Tokens/characters per request
  • Total tokens per time period
  • Concurrent requests

Response to limit breaches:

  • Return 429 Too Many Requests
  • Include Retry-After header
  • Log for abuse detection
  • Consider temporary blocks for extreme abuse

Input validation

The prompt injection threat

Attackers craft inputs to:

  • Override system instructions
  • Extract system prompts
  • Bypass content filters
  • Generate harmful outputs

Validation strategies

Structural validation:

  • Maximum input length
  • Character encoding checks
  • Format validation
  • Nested structure limits

Content validation:

  • Known attack pattern detection
  • Anomaly scoring
  • Semantic analysis
  • Keyword filtering (carefully—avoid over-blocking)

Example validation flow:

1. Check length < maximum
2. Validate encoding (UTF-8)
3. Scan for injection patterns
4. Score anomaly likelihood
5. If suspicious: flag for review or reject
6. If clean: process request

Output filtering

What to filter

Sensitive data:

Harmful content:

  • Malicious code
  • Dangerous instructions
  • Policy-violating content

Filtering approaches

Approach Pros Cons
Regex patterns Fast, predictable Limited to known patterns
ML classifiers Catches novel cases Requires maintenance
Blocklists Simple to implement Easy to bypass
Human review Most accurate Doesn't scale

Best practice: Layer multiple approaches. Use fast pattern matching first, then ML classification for uncertain cases.

Monitoring and logging

What to log

Always log:

  • Request timestamps
  • Client identifiers
  • Request metadata (size, type)
  • Response codes
  • Latency metrics

Never log:

  • Full prompt content (privacy risk)
  • API keys or tokens
  • Personal user data
  • Full model outputs

Anomaly detection

Watch for patterns indicating attacks:

  • Sudden volume spikes
  • Unusual query patterns
  • High error rates
  • Systematic probing behavior
  • Off-hours activity

Security headers and configuration

Essential headers

Content-Security-Policy: default-src 'self'
X-Content-Type-Options: nosniff
X-Frame-Options: DENY
Strict-Transport-Security: max-age=31536000

CORS configuration

Be restrictive:

  • Whitelist specific origins
  • Limit allowed methods
  • Restrict headers
  • Consider credentials carefully

Error handling

Secure error responses

Don't reveal:

  • Internal system details
  • Stack traces
  • Database errors
  • File paths
  • Version information

Do provide:

  • Generic error categories
  • Request IDs for support
  • Actionable guidance when appropriate
  • Appropriate HTTP status codes

Common mistakes

Mistake Risk Fix
Verbose errors Information disclosure Return generic messages
No rate limits Abuse, cost explosion Implement tiered limits
Logging prompts Privacy violation Log metadata only
CORS: * Cross-site attacks Whitelist origins
Key in URL Key exposure in logs Use headers

Security testing

What to test

  • Authentication bypass attempts
  • Rate limit circumvention
  • Prompt injection attacks
  • Large payload handling
  • Malformed request handling

Testing tools

  • Burp Suite for manual testing
  • OWASP ZAP for automated scanning
  • Custom scripts for AI-specific attacks
  • Load testing tools for rate limit verification

What&#39;s next

Continue building secure AI systems: