LLM Development Risks: Security Threats and Mitigation Strategies

Large language models introduce development security, operational, and compliance risks because their probabilistic behavior can blur the boundary between user data and executable instructions, making conventional deterministic security controls less effective. Key concerns include direct and indirect prompt injection, insecure handling of model-generated outputs, training-data poisoning, exposure of sensitive information, vulnerable third-party models and plugins, excessive autonomy, and unverified reliance on AI responses. AI coding assistants can additionally generate insecure code, hardcoded credentials, and unsafe or malicious dependency recommendations, requiring security review beyond syntax and functional testing. Recommended mitigations include validating inputs, sanitizing outputs, applying least-privilege access, scanning AI-generated code and dependencies, detecting secrets and malware, monitoring data flows, enforcing consistent policies across AI tools, and regularly threat-modeling LLM integrations. The material distinguishes inherent model-layer risks from application-layer risks that development teams can directly manage, and it emphasizes using static analysis, software composition analysis, reachability assessment, and audit controls before deployment, particularly for systems with customer access or autonomous permissions.

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