arXiv:2605.30521v1 Announce Type: new Abstract: Large language models must frequently process untrusted inputs, such as judging an answer from another model or running tasks like spam and harm classifiers while under adversarial pressure. These inputs are often string-formatted directly into a prompt template, leaving systems fragile to manipulation. Current LLM specs from major providers like OpenAI distinguish trustworthiness along an Instruction Hierarchy, from System messages (most trusted) to Tool Results (least trusted). A possible natural mitigation is to wrap untrusted content in a moc
The increasing sophistication and widespread deployment of large language models, particularly in sensitive applications, necessitate robust security measures against adversarial inputs.
Securing LLM prompts from untrusted inputs is critical for maintaining model integrity, preventing manipulation, and ensuring reliable operation in real-world scenarios, impacting the trustworthiness of AI systems.
This research introduces a novel mitigation strategy using mock tool calls, potentially improving the resilience of LLM systems against prompt injection and other forms of adversarial attacks.
- · LLM developers
- · AI security researchers
- · Enterprises deploying LLMs
- · Adversarial actors
- · Unsecured LLM applications
Improved security and reliability of LLM applications will lead to broader adoption in sensitive domains.
Standardization of secure prompt engineering practices will emerge, influenced by techniques like mock tool calls.
Reduced risk of AI-enabled deception and misinformation, bolstering public trust in AI-driven services.
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Read at arXiv cs.CL