AIR-006medium SeverityAI-Generated Anti-Patterns & Dynamic Execution
Token Usage and Output Length Limits Enforced on Model Calls
AI templates omit max_tokens parameters, allowing unbounded prompt loops that exhaust API credit balances.
1-Click Fix Prompt for Cursor & Claude Code
Review my codebase for "Token Usage and Output Length Limits Enforced on Model Calls" (AIR-006). Issue: AI templates omit max_tokens parameters, allowing unbounded prompt loops that exhaust API credit balances. Remediation required: Set explicit max_tokens on every LLM completion request and enforce user-level generation quotas. Please: 1. Locate any occurrences of this issue across my repository. 2. Refactor the code to follow production standards without breaking existing functionality. 3. Provide a clear command or step to test and verify the fix.
Why AI Assistants Write This Bug
AI templates omit max_tokens parameters, allowing unbounded prompt loops that exhaust API credit balances.
AI code generation prioritizes immediate syntax execution over production edge cases. When an assistant hits an undefined variable or strict type error, it frequently takes the path of least resistance rather than implementing standard pre-launch security patterns.
Code Comparison
Common AI Code PatternVulnerable
// Vulnerable AI-generated pattern for: Token Usage and Output Length Limits Enforced on Model Calls
// Problematic pattern: AI templates omit max_tokens parameters, allowing unbounded prompt loops that exhaust API credit balances.Launch-Ready Fix
// Production-ready fix for: Token Usage and Output Length Limits Enforced on Model Calls
// Recommended remediation: Set explicit max_tokens on every LLM completion request and enforce user-level generation quotas.Remediation & Verification Checklist
Remediation: Set explicit max_tokens on every LLM completion request and enforce user-level generation quotas.
- Search your codebase for pattern matching "Token Usage and Output Length Limits Enforced on Model Calls".
- Apply the recommended remediation: Set explicit max_tokens on every LLM completion request and enforce user-level generation quotas.
- Run local build and test commands to ensure no regressions.
- Verify in your browser or with curl that the issue is resolved.
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