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How do I prevent model extraction and theft attacks against my deployed LLM under OWASP LLM10?
To prevent model extraction and theft attacks, which fall under OWASP LLM10 (Unbounded Consumption), implement controls that limit query access and monitor for abuse.
Concrete controls include:
- Rate limits and quotas on model endpoints to restrict the number of queries an attacker can make.
- Token/spend caps to limit the resources an attacker can consume, thereby preventing denial-of-wallet and unbounded querying.
- Abuse detection mechanisms to identify and respond to suspicious querying patterns indicative of model extraction attempts.
- Access controls on model endpoints and weights to ensure only authorized entities can interact with or access the model's core components.
- Monitoring, measurement, analysis, and evaluation of the AI Management System (AIMS) to detect and address issues related to consumption and abuse.
Grounded in
- owasp_llm_top10
- DefenseClaw, MAESTRO, and the Security Boundary Agentic AI Has Been Missing
- Designing Agentic AI Systems with the ORCHIDEAS Framework
- LAAF: Logic-Layer Automated Attack Framework - A Systematic Red-Teaming Methodology for LPCI Vulnerabilities in Agentic Large Language Model Systems
- iso_42001
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This AI-generated answer is for guidance only — not a certification, audit, or penetration test. Grounded in the NIST AI RMF, OWASP LLM Top 10, and ISO/IEC 42001 control text; verify applicability to your environment.