model-caching-security

Prevent cross-tenant data leakage in shared LLM inference caches by auditing KV, prompt, and semantic cache layers to enforce strict tenant isolation and secure cache keys. This identifies context leakage vectors and applies measurable guardrails.

The main security risks of using semantic caches in multi-tenant inference platforms include cross-request context leakage, cache poisoning, and sidecar timing attacks. These vulnerabilities arise when shared infrastructure fails to isolate tenant cache entries.

Audit a KV cache for timing side-channel attacks by investigating cache isolation across tenants and identifying timing discrepancies during inference. Apply deployment-ready checks to mitigate sidecar risks and secure cache keys.

You can secure prompt caching layers against cache poisoning by applying hardening measures that ensure tenant isolation and validate cache keys. This provides measurable guardrails and quick-win improvements for shared inference systems.

Yes, this approach works for identifying sidecar risks in shared inference infrastructure by reviewing prompt and KV cache implementations. It proposes mitigations and deployment-ready checks to protect multi-tenant environments.