Evaluation and Hardening of LLM System Instructions Against Extraction via Encoding Attacks
System Instructions in Large Language Models (LLMs) are commonly used to enforce safety policies, define agent behavior, and protect sensitive operational context in agentic AI applications. These instructions may contain sensitive information such as API credentials, internal policies, and privileged workflow definitions, making system instruction leakage a critical security risk highlighted in the OWASP Top 10 for LLM Applications. Without incurring the overhead costs of reasoning models, many LLM applications rely on refusal-based instructions that block direct requests for system instructions, implicitly assuming that prohibited information can only be extracted through explicit queri...
| Authors | Anubhab Sahu, Diptisha Samanta, Reza Soosahabi |
|---|---|
| Primary category | cs.CR |
| Published | 1 April 2026 |
| Last updated at source | 12 August 2026 |
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