Large Language Model (LLM) Security Risks
LLM security risks include data leakage, prompt injection, model supply chain attacks, and unauthorized tool execution, requiring organizations to treat AI models as high-privilege components.
LLM security risks include data leakage, prompt injection, model supply chain attacks, and unauthorized tool execution, requiring organizations to treat AI models as high-privilege components.
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Large language model security risks encompass the vulnerabilities, attack surfaces, and threat vectors introduced when organizations integrate LLMs into their applications, workflows, and decision-making processes. These risks span data leakage, prompt manipulation, supply chain compromise of model weights, and the amplification of social engineering attacks through convincing AI-generated content.
LLMs introduce risk at multiple layers. At the data layer, models may memorize and regurgitate sensitive training data including credentials, personal information, and proprietary code. At the application layer, insufficient input validation allows prompt injection attacks that override system instructions and extract confidential context. At the integration layer, LLMs connected to tools and APIs can be manipulated into executing unauthorized actions through indirect prompt injection embedded in retrieved documents. Model supply chain risks emerge from poisoned fine-tuning datasets, compromised model registries, and malicious adapter layers distributed through public repositories.
Organizations are rapidly deploying LLMs into customer-facing applications, internal knowledge systems, and automated workflows with insufficient security review. A single prompt injection vulnerability can expose entire databases of customer information, bypass access controls, or trigger unauthorized transactions. The OWASP Top 10 for LLM Applications identifies critical risks that most development teams have never encountered, creating a significant skills gap. As LLMs become embedded in critical business processes, the blast radius of exploitation grows proportionally.
CDA addresses LLM security through missions spanning Data Protection and Sovereignty for training data governance, Vulnerability and Surface Defense for application hardening, and Security Posture and Hygiene for deployment standards. Our approach treats LLMs as high-privilege system components requiring the same rigor as database servers and authentication services -- because in practice, they often have equivalent access.
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Written by CDA Editorial
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