What OWASP LLM Top 10 (2025) - LLM10:2025 Unbounded Consumption requires
OWASP LLM Top 10 (2025) LLM10:2025 Unbounded Consumption. Unbounded Consumption refers to the process where a Large Language Model (LLM) generates outputs based on input queries or prompts. Inference is a critical function of LLMs, involving the application of learned patterns and knowledge to produce relevant responses or predictions. Attacks designed to disrupt service, deplete the target's financial resources, or even steal intellectual property by cloning a model’s behavior all depend on a common class of security vulnerability in order to succeed. Unbounded Consumption occurs when a Large Language Model (LLM) application allows users to conduct excessive and uncontrolled inferences, leading to risks such as denial of service (DoS), economic losses, model theft, and service degradation. The high computational demands of LLMs, especially in cloud environments, make them vulnerable to resource exploitation and unauthorized usage. This risk is part of the 2025 edition of the OWASP Top 10 for Large Language Model Applications, the canonical industry list of the ten most critical risks unique to LLM-based systems. Organizations deploying LLMs in production should treat each risk as both a design constraint and a continuous-monitoring obligation, with policies, automated testing, red-team evaluation, and incident response procedures defined per category.
Pillar: AI Governance & Law · Authority: OWASP Foundation, OWASP Gen AI Security Project · Version: 2.0.0 · Last updated:
Primary source: https://genai.owasp.org/llmrisk/llm102025-unbounded-consumption/
SHA-256 integrity: ee454e2da5289bececa9719b02a354e4ab208f7886152f686d360844e39df515
Primary Citations — 12 traced to source
- OWASP Top 10 for Large Language Model Applications (2025), LLM10:2025 Unbounded Consumption, Prevention and Mitigation Strategies #1 'Input Validation': Implement strict input validation to ensure that inputs do not exceed reasonable size limits.
- OWASP Top 10 for Large Language Model Applications (2025), LLM10:2025 Unbounded Consumption, Prevention and Mitigation Strategies #2 'Limit Exposure of Logits and Logprobs': Restrict or obfuscate the exposure of `logit_bias` and `logprobs` in API responses. Provide only the necessary information without revealing detailed probabilities.
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- Discovery (free): /api/v1/nodes/owasp-llm-top-10-2025-llm10-unbounded-consumption.json — 6-field metadata
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