Skip to content

FailureAtlas: A Taxonomy of Failure Modes in Multi-Provider LLM Serving Infrastructure

Jul 2026 · arXiv.org · Vol abs/2607.17525 · 0 citations · 6 references
Computer Science

TL;DR

The principal finding is that the most operationally severe failures are \emph{silent}: they return HTTP~200, pass every standard health check, and corrupt application state in ways that require semantic-level observability to detect.

Abstract

Multi-provider LLM gateways reverse proxies that route, load-balance, and rate-limit requests across foundation-model APIs have become critical production infrastructure. Yet the failure modes specific to this architectural layer remain undocumented, scattered across issue trackers and post-mortems with no unifying framework. We introduce \fa{}, a two-axis taxonomy that classifies failures by their \emph{origin layer} (Network/Transport, Streaming/Protocol, State/Session, Model~Behavior, Governance/Cost) and their \emph{detectability} (Loud vs.\ Silent). We populate this taxonomy with five verified catalog entries sourced from public bug reports and first-hand stress testing, each accompanied by a mechanistic root-cause analysis. Three entries include standalone reproduction scripts. Our principal finding is that the most operationally severe failures are \emph{silent}: they return HTTP~200, pass every standard health check, and corrupt application state in ways that require semantic-level observability to detect. Two such silent failures a concurrency race condition causing history loss and a streaming index collision corrupting tool-call payloads were discovered first-hand during \cb{} evaluation campaigns.

View source

Similar papers

Open access Aug 2026

Isolation Deployment Protocols for Re-ducing Production Risk in Multi-Datacenter Systems: An Extension of Zhao et al

These findings demonstrate that deployment decisions based on structural failure domains can substantially limit the propagation of failures beyond the intended canary population, and indicate that topology-aware rollout control and per-datacenter monitoring can substantially reduce failure propagation and containment...

Sai Joshitha Kathari · 0 citations
Book Open access Oct 2026

OpenStack-RCA-Bench: A Reproducible IaaS Root Cause Analysis Dataset

We present OpenStack-RCA-Bench, an open dataset of 64 chaos-engineering incidents for benchmarking RCA methods in OpenStack IaaS. It features a 9-phase pipeline integrating four fault injection methods (service stop, port block, process kill, config corruption), automated log collection via Loki, and ground truth defin...

И. Н. Богомолов, O. Borisenko · 0 citations
Preprint Aug 2026

eBPF Security in the Wild: Structural Concentration, Failure Mechanisms, and Discovery Gaps

Extended Berkeley Packet Filter (eBPF) is a security-critical in-kernel execution framework, yet its vulnerability landscape remains fragmented across components, semantic gaps, and testing techniques. We present an empirical study of observed eBPF vulnerabilities. We construct a multi-source dataset from Linux kernel...

Bai-Hong Chen, Ming Hua, Wei-Feng Pan et al. · 0 citations

Sensitive-Topic Leakage Through LLM Routing Metadata: Measurement and Mitigation

LLM routers pick a cheap or expensive model per request by its content, and many gateways and some cloud platforms can log that choice with content logging off. We measure this privacy channel beyond token counts, accounting for noisy labels and repeated prompts. We run pre-registered studies on 1.7 million real reques...

Teng-Ruei Chen · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.