[ISM] LLM risk mitigation (OWASP top 10):

[OWASP] Prevent unbounded consumption (SSS-02-05-10)

Implement mechanisms to prevent Large Language Models (LLMs) from being exploited through uncontrolled inputs, excessive queries, or adversarial attacks. Ensure that resource allocation, access controls, rate limiting, and monitoring systems are enforced to mitigate denial of service (DoS), financial exploitation, model theft, and system degradation.

[OWASP] Implement controls to mitigate unbounded consumption risks in LLM applications (SSS-02-05-10-01)

Unbounded consumption vulnerabilities occur when LLM applications allow excessive, uncontrolled resource usage, leading to denial of service (DoS), economic drain, model extraction, and operational degradation. Attackers can manipulate input size, processing complexity, or frequency of queries to exhaust system resources and exploit the model. To mitigate these risks, input validation and rate limiting must be implemented to prevent oversized queries and high-volume API calls. Resource allocation management and automated scaling should be enforced to ensure fair usage and maintain performance under load spikes. Timeouts, throttling, and sandboxing must be utilized to restrict model access to external resources and internal systems, reducing potential attack surfaces. To prevent model extraction, techniques like logit restriction, watermarking, and adversarial robustness training should be applied. Strict access control policies and centralized ML model inventories will ensure that only authorized entities can access and deploy models, minimizing unauthorized use or replication risks. Comprehensive monitoring and anomaly detection will enable rapid response to suspicious activity and emerging threats. By implementing multi-layered defenses, organizations can protect LLM applications from financial exploitation, service degradation, and intellectual property theft while maintaining reliable, controlled operations.

Operations

ID	Operation	Description	Phase	Agent
SSS-02-05-10-01-01	Enforce rate limiting and request quotas	Implement strict limits on the number of requests per user or API key to prevent excessive queries from overwhelming system resources.	Deployment	AI engineers, Security team, Infrastructure team
SSS-02-05-10-01-02	Implement input validation and query restrictions	Validate input sizes, content, and formatting to prevent oversized or adversarial queries from consuming excessive resources.	Development	Development team, Security team
SSS-02-05-10-01-03	Monitor resource consumption and detect anomalies	Deploy real-time logging and monitoring tools to track computational usage, detect unusual patterns, and prevent excessive resource drains.	Post-deployment	Operation team, Security team, DevOps team
SSS-02-05-10-01-04	Prevent unauthorized model extraction with watermarking and logging	Use watermarking to detect unauthorized use of generated content and log API interactions to monitor for potential model theft attempts.	Deployment	Security team
SSS-02-05-10-01-05	Enforce privilege-based access controls for model interactions	Restrict LLM access using role-based access control (RBAC) and enforce least-privilege policies for API and system-level permissions.	Preparation	Security team

References

Industry framework	Academic work	Real-world case
Information Security Manual (ISM-1923) OWASP Top 10 for LLM OWASP Top 10 for LLM (LLM10:2025)