Silent Failures: Hidden Anomalies in Systems

• Silent failures are defined as hidden anomalies where individual components report success while overall system objectives are not met.
• They arise from factors like stateless designs, isolated function execution, and misaligned policies in serverless and AI-driven systems.
• Detection requires advanced observability techniques, multi-source data fusion, and explicit verification of behavioral contracts to uncover these failures.

Searching arXiv for the specified paper and closely related work on silent failures to ground the article with current references. Silent failures are failures, anomalies, or incorrect outputs that are not accompanied by any explicit error notification, exception, timeout, crash, or tool failure flag, while the surrounding system still appears to complete its task successfully. In the cited literature, this phenomenon is formalized in multiple ways: serverless pipelines whose individual functions return success while end-to-end SLOs are violated (Nguyen et al., 7 Jul 2025); AI components that produce confident but incorrect outputs without an internal alarm (Yang et al., 25 Oct 2025); tool-using LLM agents that execute policy-violating writes without error and leave the environment in a wrong state (Reddy et al., 8 Jul 2026); medical image models that output plausible but incorrect predictions under distribution shift (Gonzalez et al., 2022); and CI jobs that signal success while failing to fully or correctly complete their intended tasks (Aïdasso et al., 17 Sep 2025). Across these settings, the central property is not merely error, but the combination of incorrect behavior, absent actionable signaling, and delayed or displaced observability.

1. Definitions and formal structure

A precise serverless definition treats a pipeline as a directed acyclic graph $G=(V,E)$ of functions $V$ and triggers $E$. A silent failure occurs at time $t$ when per-function success predicates satisfy $s_v(t)=1$ for most $v\in V$, but the end-to-end objective $O(t)$ leaves its acceptable region $R_O$ without any explicit alert:

$$I_{\text{silent}}(t) = I\left[(\forall v\in V: s_v(t)=1)\wedge (O(t)\notin R_O)\wedge (\text{alerts}(t)=\emptyset)\right].$$

This definition makes explicit that local correctness and global correctness can diverge in stateless systems (Nguyen et al., 7 Jul 2025).

In tool-using LLM agents, the same logic is expressed at the action boundary. Let $S_t$ denote the environment state, $V$0 a proposed tool call, $V$1 the tool transition function, and $V$2 a policy predicate. A silent wrong-state failure occurs when $V$3 and the tool nevertheless executes the call successfully, yielding $V$4 with no error observed. The failure is therefore not an exception condition but a successful state transition to a prohibited state (Reddy et al., 8 Jul 2026).

In instruction-following LLMs, silent failure is tied to pre-commitment detectability. Let $V$5 denote the token position where generation direction becomes determined, and let $V$6 indicate whether a predictive conflict signal was observed before commitment. Silent commitment failure is then

$V$7

that is, an incorrect committed output with no prior detectable warning signal (Ruddell, 22 Mar 2026).

A different formalization appears in latent reasoning, where silent failures are “confident yet incorrect outputs.” Confidence is operationalized by activation stability across independent runs:

$V$8

with the silent failure rate defined as

$V$9

This formulation separates correctness from computational reliability and treats stable-but-wrong behavior as a distinct regime (Sahoo et al., 3 Mar 2026).

2. Mechanisms and manifestations

The supplied studies describe silent failures as arising from architecture, observability, and task structure rather than from a single failure mechanism. In serverless computing, the relevant mechanisms include statelessness and short-lived execution, isolation of functions, ephemeral scaling, and limited observability. These conditions make data schema drift, event ordering/race conditions, partial pipeline drops, cold start amplification, transient resource limits, misconfigured triggers, IAM permission glitches, and cross-function correlation gaps difficult to detect at single-function granularity (Nguyen et al., 7 Jul 2025).

In multi-agentic AI systems, silent failures are cataloged as drift, cycles/loops, omissions or missing details, tool failures captured via span error statuses, and context propagation failures. The crucial point is that these trajectories often remain fluent and operationally plausible even when they deviate from expert-defined expected paths $E$0 or repeat tools and agents in anomalous ways (Pathak et al., 6 Nov 2025).

A production LLM-agent runtime study derives a five-class mechanism-oriented taxonomy: environment and platform quirks, design-assumption mismatches, error swallowing and dilution, chained hallucination and fabrication, and operational omission and forensic blind spots. Its distinctive contribution is the notion of fail-plausible: the system does not merely suppress an error signal, but transforms internal error into coherent, contextually appropriate, and false output delivered to the user (Wu, 12 Jun 2026).

The domain breadth is substantial:

Domain	Silent failure form	Representative source
Serverless computing	Per-function success with pipeline-level SLO violation	(Nguyen et al., 7 Jul 2025)
Safety-critical AI	Confident but incorrect output without internal alarm	(Yang et al., 25 Oct 2025)
Tool-using LLM agents	Policy-violating write with no tool error	(Reddy et al., 8 Jul 2026)
Medical imaging	Plausible segmentation or classification under OOD shift	(Gonzalez et al., 2022)
CI	Green job that did not fully complete intended work	(Aïdasso et al., 17 Sep 2025)

Hardware studies describe silent data corruption as incorrect circuit operation in regions without check logic, producing wrong numerical results without crashes, hangs, device errors, or health alarms. These corruptions can flip a single bit, alter instruction execution, or silently reroute a training trajectory to a different optimum (Dixit et al., 2022, Ma et al., 17 Feb 2025). In robotics, silent manipulation failures appear as false successes: episodes marked successful by the robot’s own check even though the task outcome is wrong (Bedi, 2 Jun 2026). In PINNs, the mechanism is parameter misspecification: the network satisfies the wrong PDE well, so residual loss remains low while the learned solution is physically incorrect (McShannon et al., 23 Jun 2026).

3. Observability and detection

A recurrent theme is that silent failures require observability strategies that do not rely on explicit failure notifications. In serverless systems, the proposed observability stack combines function-level metrics such as latency $E$1, error rate $E$2, cold start probability $E$3, timeout rate $E$4, traffic variables $E$5, $E$6, $E$7, logs and traces with correlation IDs, config or CI/CD events, cloud billing, and edge telemetry. Detection is then framed as multi-source fusion with streaming detectors such as EWMA, CUSUM, likelihood-based scoring, and KL divergence, plus DAG-aware correlation over node and edge features (Nguyen et al., 7 Jul 2025).

In large-scale cloud systems, non-intrusive event analysis learns runtime monitoring rules from fault-free executions and correlates events without manual ID propagation. On OpenStack, this session-aware approach achieved an F1 score of $E$8 and accuracy of $E$9, compared to $t$0 and $t$1 for OpenStack logging mechanisms, and reduced average detection latency by approximately $t$2 seconds (Cotroneo et al., 2023). At network scale, probe-based silent-failure detection is formalized as a scheduling problem over elements $t$3 and probes $t$4, with memoryless and deterministic schedules optimized for SUM or MAX objectives. This literature treats detection and localization as separate phases because localization is significantly more resource-intensive (Cohen et al., 2013).

Medical imaging studies address silent failures as an OOD and uncertainty problem. For Covid-19 lesion segmentation, a lightweight post-hoc method models encoder features with a multivariate Gaussian and assigns a Mahalanobis score

$t$5

flagging cases whose activations lie far from the in-distribution manifold. The method effectively detects far- and near-OOD samples across chest CT, hippocampus MRI, and prostate MRI without retraining (Gonzalez et al., 2022). For WMH segmentation, the combination of Stochastic Segmentation Networks with Deep Ensembles yields the highest Dice and lowest AVD% on in-domain and OOD data, while uncertainty maps localize poor segmentations and support downstream Fazekas classification (Philps et al., 2024).

For completed LLM-agent traces in the silent-failure regime, REFLECT reframes detection as intervention-supported error attribution. A trace $t$6 is silently failed when it completes normally but $t$7. REFLECT identifies a candidate error step, constructs a diagnosis-specific patch, replays the trace from a rollback point, and adopts the earliest rollback point $t$8 for which the verified outcome flip

$t$9

holds. Detection is therefore operationalized through controlled replay rather than through purely narrative auditing (Lin et al., 8 Jun 2026).

4. Mitigation and assurance architectures

Mitigation strategies in the supplied literature are strongly domain-specific, but they share a common design principle: move from implicit trust in nominal outputs toward explicit verification of behavioral contracts.

For safety-critical AI, FAME wraps an opaque AI component with a formally specified safety envelope enforced by runtime monitors synthesized from Signal Temporal Logic. In the autonomous-vehicle case study, monitors evaluate properties such as

$s_v(t)=1$0

publish a Boolean violation flag and a robustness margin $s_v(t)=1$1, and trigger fail-safe, fail-operational, or fail-degraded mitigation. In challenging scenarios, the monitored perception system violated the property in $s_v(t)=1$2 of $s_v(t)=1$3 runs, and FAME detected $s_v(t)=1$4 of these violations, a $s_v(t)=1$5 detection rate, while raising zero false alarms in $s_v(t)=1$6 nominal runs (Yang et al., 25 Oct 2025).

For policy-permissive tools, deterministic pre-execution gates act at the action boundary. Each gate computes a read-only predicate $s_v(t)=1$7, and the composite decision is

$s_v(t)=1$8

A four-gate suite covering cancellation eligibility, baggage allowance, passenger count immutability, and must-read-before-write raised full-benchmark success on the budget airline agent from $s_v(t)=1$9 to $v\in V$0 on gpt-4o-mini, with effects concentrated on the $v\in V$1 tasks where the gates fired (Reddy et al., 8 Jul 2026).

For long-lived agent runtimes, the PIG Engine and ADE protocol suite impose deterministic governance outside the probabilistic loop. The underlying theory models disorder as

$v\in V$2

with $v\in V$3 defined from cross-agent transmission fidelity $v\in V$4, task accuracy $v\in V$5, and cross-session knowledge consistency $v\in V$6. PIG periodically evaluates deterministic predicates over system state, and ADE protocols such as BCP, TLC, DCM, CADVP, and PIP perform message confirmation, rollback, drift correction, quality gating, and invariant enforcement. The study reports that governance reduces $v\in V$7 by $v\in V$8–$v\in V$9 and extends reliability windows up to approximately $O(t)$0 longer before failure thresholds (Liu, 6 Jun 2026).

Other mitigation architectures target specialized domains. In partially automated driving, Prospective Situation Awareness Enhancement interfaces delivered through an AR HUD improve takeover performance in silent automation failures by strengthening driver situation awareness; Environment Perception cues were most effective for SA, while Planned Maneuver cues were superior for trust (Wang et al., 20 Apr 2026). In PINNs, six candidate defenses fail to detect corruption across all regimes, but a post-hoc parameter-space loss sweep without retraining recovers the true training parameter across Burgers, cavity, and convection–diffusion systems (McShannon et al., 23 Jun 2026). In CI, mitigation centers on fail-fast scripting, explicit artifact verification, cache validation, test result assertions, security scan hardening, structured logging, and runner configuration checks because silent failures often arise from artifact operation errors, caching errors, and ignored exit codes (Aïdasso et al., 17 Sep 2025).

5. Evaluation regimes and empirical findings

Silent-failure research uses heterogeneous evaluation protocols because the failure object differs by domain. Serverless anomaly detection emphasizes false positive and false negative rates, detection latency, overhead, budget impact $O(t)$1, and SLO adherence, often with ROC and PR curves under multi-tenant noise (Nguyen et al., 7 Jul 2025). In multi-agentic AI trajectory detection, binary anomaly classification over OpenTelemetry-derived features reaches up to $O(t)$2 accuracy for XGBoost and up to $O(t)$3 accuracy for SVDD, with subtle drift remaining the dominant false-negative mode (Pathak et al., 6 Nov 2025).

In CI, silent failures are operationalized through reruns of successful jobs. Across $O(t)$4 jobs in $O(t)$5 industrial projects, approximately $O(t)$6 of successful jobs were rerun, $O(t)$7 of these reruns occurred after more than $O(t)$8 hours, and reruns following success accounted for $O(t)$9 of total rerun server time. The associated mixed-effects logistic regression achieved ROC AUC $R_O$0, AUC-PR $R_O$1, and Brier Score $R_O$2 (Aïdasso et al., 17 Sep 2025).

Several studies explicitly show that apparent task success, low loss, or benchmark accuracy are not reliable surrogates for failure absence. In latent reasoning, Qwen2.5-Math-7B achieves $R_O$3 accuracy on a $R_O$4-item GSM8K subset, yet $R_O$5 of all predictions are silent failures and $R_O$6 of correct predictions are computationally inconsistent pathways (Sahoo et al., 3 Mar 2026). In medical image classification, none of the benchmarked confidence scoring functions reliably prevents silent failures across corruption, acquisition, and manifestation shifts; MCD-MSR is strongest overall, but substantial silent failures remain in three of four tasks (Bungert et al., 2023). In PINNs, poisoned models can match or beat the clean-model training loss while differing from the correct solution by up to $R_O$7 in the fixed sweep and up to $R_O$8 under adversarial search (McShannon et al., 23 Jun 2026).

The medical-imaging literature also illustrates that evaluation must track downstream utility, not only primary-task metrics. In WMH segmentation, incorporating uncertainty information improves median class balanced accuracy for Fazekas classification from $R_O$9 to $$I_{\text{silent}}(t) = I\left[(\forall v\in V: s_v(t)=1)\wedge (O(t)\notin R_O)\wedge (\text{alerts}(t)=\emptyset)\right].$$0 in Deep WMH regions and from $$I_{\text{silent}}(t) = I\left[(\forall v\in V: s_v(t)=1)\wedge (O(t)\notin R_O)\wedge (\text{alerts}(t)=\emptyset)\right].$$1 to $$I_{\text{silent}}(t) = I\left[(\forall v\in V: s_v(t)=1)\wedge (O(t)\notin R_O)\wedge (\text{alerts}(t)=\emptyset)\right].$$2 in Periventricular WMH regions, relative to spatial features without uncertainty (Philps et al., 2024). In robotic manipulation, false-success recall is the relevant curation metric: in cube transfer, proprioception alone recovers $$I_{\text{silent}}(t) = I\left[(\forall v\in V: s_v(t)=1)\wedge (O(t)\notin R_O)\wedge (\text{alerts}(t)=\emptyset)\right].$$3 of false successes, whereas in peg insertion proprioception recovers only $$I_{\text{silent}}(t) = I\left[(\forall v\in V: s_v(t)=1)\wedge (O(t)\notin R_O)\wedge (\text{alerts}(t)=\emptyset)\right].$$4 and a vision detector reaches $$I_{\text{silent}}(t) = I\left[(\forall v\in V: s_v(t)=1)\wedge (O(t)\notin R_O)\wedge (\text{alerts}(t)=\emptyset)\right].$$5 (Bedi, 2 Jun 2026).

6. Limits, misconceptions, and open problems

Several cited studies reject the sufficiency of familiar reliability proxies. Benchmark accuracy does not predict governability in instruction-following LLMs; a smaller model can be governable under greedy decoding while a larger instruction-tuned model remains silent with zero warning margin (Ruddell, 22 Mar 2026). Low residual loss in PINNs certifies only that the network has learned the encoded equations, not that the encoded equations are correct (McShannon et al., 23 Jun 2026). In medical image classification, calibration or softmax confidence alone does not reliably prevent silent failures under realistic biomedical distribution shifts (Bungert et al., 2023).

The defenses are likewise bounded. FAME’s protection is only as strong as the authored properties and observable signals (Yang et al., 25 Oct 2025). Deterministic gates help when tools are policy-permissive and policies are state-decidable, but they add little in self-enforcing domains (Reddy et al., 8 Jul 2026). In WMH segmentation, even with SSN-Ens and matched uncertainty thresholds, $$I_{\text{silent}}(t) = I\left[(\forall v\in V: s_v(t)=1)\wedge (O(t)\notin R_O)\wedge (\text{alerts}(t)=\emptyset)\right].$$6–$$I_{\text{silent}}(t) = I\left[(\forall v\in V: s_v(t)=1)\wedge (O(t)\notin R_O)\wedge (\text{alerts}(t)=\emptyset)\right].$$7 of small lesions remain neither segmented nor marked uncertain (Philps et al., 2024). In production agent runtimes, retrospective audit prevented $$I_{\text{silent}}(t) = I\left[(\forall v\in V: s_v(t)=1)\wedge (O(t)\notin R_O)\wedge (\text{alerts}(t)=\emptyset)\right].$$8 of incidents ex ante but blocked $$I_{\text{silent}}(t) = I\left[(\forall v\in V: s_v(t)=1)\wedge (O(t)\notin R_O)\wedge (\text{alerts}(t)=\emptyset)\right].$$9 in regression, leading to the conclusion that audit is a regression engine, not a prediction engine (Wu, 12 Jun 2026).

Open problems are correspondingly structural. Serverless research emphasizes context-rich correlation, multi-source fusion under privacy constraints, adaptive detectors for ephemeral functions, benchmark creation for serverless-specific anomalies, robustness against adversarial behaviors, and edge–cloud split optimization (Nguyen et al., 7 Jul 2025). Agentic-AI studies call for richer anomaly taxonomies, causal detection of drift, multi-agent coordination signals, and robust online detection under label scarcity (Pathak et al., 6 Nov 2025). Production-runtime work argues that the longest-lived failures inhabit seams between components, where no test runs, and recommends converting point fixes into meta-rules and then into mechanized scanners (Wu, 12 Jun 2026).

Silent failures are therefore not a single bug class but a cross-domain reliability pattern defined by wrong behavior, missing actionable signaling, and delayed discovery. The strongest results in the supplied literature do not eliminate the phenomenon; they make it visible, attributable, and governable. In serverless platforms this means DAG-aware, multi-source anomaly detection (Nguyen et al., 7 Jul 2025); in safety-critical AI it means formally synthesized monitors (Yang et al., 25 Oct 2025); in tool-using agents it means deterministic gates at the action boundary (Reddy et al., 8 Jul 2026); in medical imaging it means uncertainty-aware triage and OOD detection (Philps et al., 2024); and in long-lived LLM runtimes it means engineering systems whose failures are loud, attributable, and boring (Wu, 12 Jun 2026).