Unbounded AI Consumption

Unbounded AI consumption occurs when AI systems are used or queried without appropriate limits—resulting in excessive generation, runaway processes, or harmful automation. This issue is common in language models, AI agents, and orchestration frameworks where output triggers further actions or downstream tasks.

Examples include:

• AI assistants that endlessly call APIs or tools.
• Agents generating thousands of queries or documents without checks.
• LLM systems that hallucinate harmful advice, which is acted upon without validation.
• Cost overruns caused by recursive or expansive prompt chains.

These risks arise from a lack of:

• Consumption boundaries: No limit on tokens, iterations, or calls.
• Human-in-the-loop (HITL) oversight.
• Prompt and output validation before execution.
• Rate limiting or usage governance.

Unbounded consumption can lead to:

• Security risk: Agents triggering unauthorized actions.
• Performance degradation: Resource exhaustion and service slowdowns.
• Financial cost: Unexpected compute or API usage bills.
• Trust erosion: Delivering unverified or unsafe results to users.

This is especially problematic in “agentic” systems, where LLMs have autonomy over workflows and tool selection. Without constraint mechanisms, even well-intentioned AI can spiral into inefficient or harmful behavior.

How PointGuard AI Addresses This:
PointGuard AI enforces boundaries around model behavior and ensures that AI systems remain efficient, governed, and safe—preventing runaway costs, hallucinated chains, or abuse of autonomous tools in live environments.

OWASP LLM10:2025 Unbounded Consumption

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