arXiv:2604.01039v2 Announce Type: replace-cross Abstract: 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 in
The proliferation of LLM applications necessitates robust security measures as sensitive data becomes embedded in system instructions, making security vulnerabilities increasingly critical.
This framework directly addresses a significant security vulnerability (OWASP Top 10) in LLM applications, which, if unmitigated, poses substantial risks to data integrity, privacy, and operational security.
The ability to systematically evaluate and harden LLM system instructions against encoding attacks enables more secure and reliable deployment of AI agents in sensitive contexts.
- · AI developers
- · Cybersecurity firms
- · Enterprises deploying LLMs
- · Open-source AI security community
- · Cyber attackers
- · Organizations with lax LLM security
- · Unsecured LLM applications
Reduced incidents of sensitive information leakage from LLM system instructions.
Increased trust and accelerated adoption of agentic AI applications across various industries.
The development of industry-standard security protocols and certifications for LLM system instruction integrity.
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Read at arXiv cs.AI