Beyond DoS: How Unbounded Consumption is Reshaping LLM Security

The 2025 OWASP Top 10 list for Large Language Models (LLMs) has introduced "Unbounded Consumption" as a critical risk, replacing the previous focus on Model Denial of Service (DoS). This shift acknowledges the broader range of threats LLMs face, such as excessive and uncontrolled inferences that can lead to service degradation, financial losses, and even model theft. Unlike traditional DoS attacks that target network bandwidth, unbounded consumption exploits how AI models process requests, overwhelming systems with crafted prompts that drain computational resources. To mitigate these risks, organizations are encouraged to implement strategies such as rate limiting, input validation, resource management, and real-time monitoring. Tools and technologies like adaptive rate limiting, anomaly detection, and scalable infrastructure are essential for defending against these attacks. Regular audits, red-teaming exercises, and adherence to OWASP's guidelines further bolster security against evolving threats. The discussion highlights the importance of layered defenses to maintain service availability and protect against sophisticated attacks on LLMs.