Cooldown Landmines: Cross-Tenant Interference Attacks on LLM Gateways
cs.CR
Submitted: 2026-10-04
Updated: 2026-10-04
Comments: 24 pages, 7 figures. Main text in ICLR format; includes appendices
Code: https://github.com/aws-samples/sample-resilient-llm-inference
License: http://creativecommons.org/licenses/by/4.0/
The gist: LLM gateways enforce separate tenant quotas while sharing model deployments and cooldown records that temporarily exclude failing backends.
Abstract
LLM gateways enforce separate tenant quotas while sharing model deployments and cooldown records that temporarily exclude failing backends. However, a tenant's request failure can update these shared records and restrict other tenants' access to serviceable deployments. We identify two attacks that exploit this gap in LiteLLM. The first uses requests rejected at the key's requests-per-minute (RPM) limit: caller-supplied identifiers for known registered deployments reach failure handling, allowing two rejected requests to redirect another tenant to fallback with zero upstream calls from those requests. The second uses admitted traffic to create cooldown records that persist after backend capacity recovers. To address these failures, we design an origin check that blocks deployment updates from key RPM rejections and tenant-scoped cooldown that preserves the triggering tenant's back-off while retaining shared records for backend faults. Experiments with authenticated proxies and self-hosted vLLM demonstrate that the attacks can force fallback or denial while deployments remain serviceable. Across five paired four-worker runs, tenant scoping reduces victim fallback after recovery from 54/60 to 0/60, while increasing attempts against exhausted shared quotas. These findings show that tenant isolation must cover both request admission and the failure handling that governs shared deployment availability.
Sources
- Governing the KV Cache: Preventing Timing Side-Channel Leakage in Multi-Tenant LLM Inference
- Do System Prompts Leave Behavioral Fingerprints? A Large-Scale Empirical Study of Clone Detection via Output Similarity
- DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models
- ComplianceGate: Classifier-Gated Multi-Tier LLM Routing for Inference in Regulated Industries
- PathMark: Protecting Intellectual Property of Mixture-of-Expert LLMs via Path Watermarks
- Learning Normal Diffusion Dynamics for Backdoor Defense in Text-to-Image Models
- Life-Cycle Routing Vulnerabilities of LLM Router
- Trust the Brand, Lose Control: How Identity Hijacks LLM Agent Orchestration
- "You're Right, Let Me Fix It": How LLM Agents Damage Correct Work When Falsely Accused
- ContinuityBench: A Benchmark and Systems Study of Stateful Failover in Multi-Provider LLM Routing
- RerouteGuard: Understanding and Mitigating Adversarial Risks for LLM Routing
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