arXiv:2605.30613v1 Announce Type: cross Abstract: Over the past year, prompt caching in Large Language Models (LLMs) has become increasingly more popular across inference APIs. Prompt caching helps save precious compute resources and speeds up response times by reusing parts of the KV cache of a specific prompt for another request. However, many implementations of prompt caching are not secure against timing attacks or even basic metadata disclosure. Gu et al. (ICML 2025) develop a method to audit prompt caching in LLMs. This paper investigates whether OpenRouter's API gateway architecture int
The increasing adoption of prompt caching in LLM inference APIs, driven by efficiency needs, has created new vulnerabilities that researchers are now actively investigating.
This highlights critical security and privacy concerns within the rapidly evolving AI infrastructure, affecting trust and the secure deployment of LLMs, especially in sensitive applications.
The understanding of prompt cache security shifts from a theoretical concern to a practical auditing challenge, forcing API providers to re-evaluate their implementations and leading to more robust security standards.
- · Cybersecurity researchers
- · Organizations prioritizing AI security
- · Secure LLM API providers
- · LLM API providers with insecure caching
- · Users of vulnerable LLM APIs
- · Attackers relying on basic timing attacks
Immediate audits and patch releases for vulnerable prompt caching implementations by LLM API providers.
Development of industry best practices and standards for secure prompt caching in AI gateways.
Increased regulatory scrutiny on AI security and privacy, leading to compliance requirements for LLM deployments.
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