LLMs Invent Context When Not Provided, Leading to Security Risks

A new analysis highlights a critical issue in how large language models (LLMs) operate: when context is not explicitly provided, models invent it based on their training data, often leading to insecure or incorrect outputs. This phenomenon, termed "Delta's Law 4," means LLMs complete prompts using patterns from the vast public internet corpus rather than the specific, intended constraints of the user. To mitigate this, developers must provide comprehensive context covering domain semantics, system invariants, environmental realities, and failure postures, rather than simply adding more words. Adopting this discipline allows models to accurately reflect user intentions, leading to improved security and performance, as evidenced by rising AI-attributable CVEs and the need for explicit security measures. AI

IMPACT Developers must provide explicit context to LLMs to ensure accurate and secure outputs, preventing the models from inventing potentially harmful information.

RANK_REASON The item discusses a concept and its implications based on analysis of existing reports and a published paper. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

• Delta: Closing the Specification Gap
• Delta's Law 4
• Georgia Tech
• Sandeep Dhuri
• Spring 2026 GenAI Code Security Update
• Veracode
• Verizon DBIR

• safety
• paper

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