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The protection of large language models and applications built on them from attacks such as prompt injection, jailbreaking, data exfiltration, and training data extraction. LLM security involves input filtering, output validation, sandboxing model access to sensitive systems, and monitoring for misuse across chat interfaces and API integrations.
Organizations rapidly integrating LLMs into their products and workflows face novel attack vectors that traditional security controls do not cover. Security engineers must understand prompt injection and output manipulation to protect these systems. OWASP now maintains a Top 10 list specifically for LLM risks.
Cross-vertical bridge
The Applied AI glossary covers a parallel large language model term used at the AI-system-design layer.
Read about Large Language Model in Applied AI →Citation index · auto-derived from course content
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The protection of large language models and applications built on them from attacks such as prompt injection, jailbreaking, data exfiltration, and training data extraction. LLM security involves input filtering, output validation, sandboxing model access to sensitive systems, and monitoring for misuse across chat interfaces and API integrations.
Organizations rapidly integrating LLMs into their products and workflows face novel attack vectors that traditional security controls do not cover. Security engineers must understand prompt injection and output manipulation to protect these systems. OWASP now maintains a Top 10 list specifically for LLM risks.
Cybersecurity professionals who work with LLM Security include Security Engineer, Security Architect, Penetration Tester. These roles apply LLM Security knowledge within the Emerging Technology Security domain.
Definitions are original explanations written for career development purposes. For authoritative technical definitions, refer to NIST, ISO, or the relevant standards body.
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