Security / Resilience / Autonomous Systems
H-R01 — A Comprehensive Failure Ontology for Autonomous Machine Systems and Critical Infrastructure
Report summary
The integration of autonomous machine systems, multi-agent artificial intelligence, distributed services, and cyber-physical systems (CPS) into critical infrastructure introduces unprecedented complexity and novel failure surfaces [reasoned inference]. Historical reliability engineering frameworks a
Key topics
- Security / Resilience / Autonomous Systems
- Security
- Resilience
- Autonomous Systems
- AI
- Agentic Web
- .NET
- Runtime
- Semantic Systems
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1. Executive Decision Brief
The integration of autonomous machine systems, multi-agent artificial intelligence, distributed services, and cyber-physical systems (CPS) into critical infrastructure introduces unprecedented complexity and novel failure surfaces \[reasoned inference\]. Historical reliability engineering frameworks are highly domain-specific, resulting in semantic impedance mismatches when integrating hardware safety engineering, site reliability engineering (SRE), nuclear probabilistic risk assessment, and autonomous agent threat modeling \[institutional analysis\]. This report establishes a unified, implementation-oriented failure ontology designed to serve as the conceptual foundation for a public knowledge site dedicated to resilience and recovery \[technical proposal\]. The architecture normalizes terminology across divergent engineering traditions, resolving critical namespace collisions—such as the overloading of the terms "incident" and "systematic failure"—and structures failure data into a rigorous, machine-readable format \[technical proposal\]. The framework distinguishes foundational causality chains, mapping the transition from latent faults to active errors, and ultimately to service failures, while extending traditional taxonomies to encompass Byzantine faults, split-brain conditions, semantic drift, and emerging agentic failure modes such as goal hijacking and inter-agent trust escalation \[technical proposal\]. Research indicates that without considering information quality and evidence integrity, cyber-physical systems remain vulnerable to faults arising from inaccurate or outdated telemetry \[peer-reviewed research finding\]1. This ontology applies Dempster-Shafer theory to manage conflicting evidence, providing a robust mathematical model for uncertainty \[reasoned inference\].
2. Definitions and Scope
To minimize ambiguity and prevent catastrophic misconfigurations during incident response, this ontology enforces strict semantic boundaries across the failure continuum \[policy proposal\]. The following canonical definitions form the core of the ontology, resolving inconsistencies found across dependable computing, SRE, and safety engineering \[institutional analysis\]. (Research Cutoff: August 2026). The fundamental causality chain in dependable computing is defined as the progression from Fault to Error to Failure \[established standard or law\]2.
- Fault: The adjudged or hypothesized algorithmic, physical, or interaction-based cause of an error \[established standard or law\]3. Faults may remain dormant for extended periods before activation \[established standard or law\].
- Error: A deviation in the internal state of a system that may lead to a subsequent failure \[established standard or law\]3. It is the manifestation of a fault within the system boundary \[established standard or law\].
- Failure: An event that occurs when the delivered service deviates from its specified correct service, representing a transition from correct to incorrect service delivery \[established standard or law\]4.
- Incident: An event requiring human or automated emergency response to prevent, mitigate, or recover from a failure. Unlike IFIP WG 10.4, which treats incidents and failures synonymously, this ontology adopts the Site Reliability Engineering definition where incidents encompass both active outages and near-misses \[observed deployment or practice\]5.
- Outage: A total or partial cessation of service delivery that is actively visible to the end user or upstream consuming system \[current official policy\].
- Degradation: A state where a system continues to operate but with reduced performance, capacity, or reliability margins \[established standard or law\]. Graceful degradation ensures that the system handles tasks as well as possible under constrained circumstances \[peer-reviewed research finding\]6.
- Collapse: The rapid, unrecoverable failure of a system's core architecture, typically resulting from the depletion of all reserve capacity, redundancy, and mitigation mechanisms \[reasoned inference\].
- Catastrophe: A severe failure resulting in irreversible environmental harm, loss of life, or total organizational destruction, aligning with MIL-STD-882E and ARP4761 severity classifications \[established standard or law\]7.
- Latent Defect: A dormant fault introduced during the design, coding, or manufacturing phase that has not yet been activated by a specific system state or environmental trigger \[established standard or law\].
- Common-Cause Failure (CCF): A dependent failure in which two or more component fault states exist simultaneously (or within a short interval) and are the direct result of a shared root cause and coupling mechanism \[established standard or law\]9.
- Cascading Failure: A failure in a system of interconnected parts in which the failure of one part triggers a positive feedback loop (e.g., load redistribution, retry storms), leading to the successive failure of other parts \[peer-reviewed research finding\]6.
- Dependency Failure: A failure induced by the outage or erratic behavior of an external service, library, or hardware component upon which the primary system relies \[observed deployment or practice\].
- Timing Failure: A state where a system produces the correct output, but outside the specified operational time window (either prematurely or too late), often resulting in real-time control instability \[established standard or law\]1.
- Authority Failure: A condition where an entity retains cryptographic or logical access rights that should have been revoked, or where a subordinate entity successfully usurps the permissions of a superior entity via identity confusion \[technical proposal\]13.
- Evidence Failure: A breakdown in the diagnostic, logging, or monitoring subsystems resulting in the inability to verify system state, or the deliberate generation of falsified telemetry \[technical proposal\].
- Model Failure: A failure in an AI/ML system where outputs diverge from reality or safety constraints due to concept drift, semantic drift, or adversarial input \[peer-reviewed research finding\]15.
- Recovery Failure: The failure of a designated safety mechanism, backup system, or redundant cluster to successfully assume the load of a failed primary component during a failover event \[reasoned inference\].
Parent-Child Relationships and Prohibited Conflations
To maintain referential integrity within the ontology, hierarchical relationships are strictly defined, and common industry conflations are explicitly prohibited \[technical proposal\].
| Parent Class | Child Class | Prohibited Conflation | Canonical Distinction |
|---|---|---|---|
| Threat | Fault | Fault conflated with Error | A fault is the underlying defect; an error is the activated state deviation resulting from the fault \[established standard or law\]3. |
| Event | Incident | Incident conflated with Failure | An incident is an operational response event; a failure is a technical state deviation. Incidents include near-misses \[observed deployment or practice\]. |
| Hardware Defect | Random Failure | Random conflated with Systematic | Random failures result from physical degradation; systematic failures are deterministic design/software flaws \[established standard or law\]16. |
| Network Event | Split-Brain | Split-Brain conflated with Crash | A crash removes a node; split-brain leaves multiple nodes active but logically partitioned, creating divergent data \[established standard or law\]18. |
| ML Degradation | Semantic Drift | Semantic conflated with Concept Drift | Concept drift changes statistical target properties; semantic drift changes real-world meaning while data structure remains static \[peer-reviewed research finding\]15. |
Glossary Candidates for Knowledge Site
The following table provides the 30 required glossary candidates, formatted for direct integration into the public resilience knowledge site \[technical proposal\].
| Term | Definition for Public Glossary |
|---|---|
| Active Fault | A fault that is currently generating an error within the system state \[established standard or law\]. |
| Alpha Factor Method | A statistical parametric model used in nuclear PRA to estimate common-cause failure probabilities \[current official policy\]9. |
| ASIL | Automotive Safety Integrity Level, a risk classification scheme defined by ISO 26262 \[established standard or law\]21. |
| Blind Delegation | Assigning tasks to a subordinate AI agent without cryptographically verifying its identity \[institutional analysis\]13. |
| Bulkhead Pattern | An architectural design isolating subsystems to prevent cascading failure propagation \[observed deployment or practice\]6. |
| Byzantine Fault | A failure mode where a component behaves arbitrarily and presents conflicting information to different observers \[peer-reviewed research finding\]12. |
| Circuit Breaker | A software pattern that temporarily halts requests to a failing service to prevent overload \[observed deployment or practice\]6. |
| Concept Drift | The statistical properties of a target variable changing over time in unforeseen ways, invalidating ML models \[peer-reviewed research finding\]15. |
| Confused Deputy | A security failure where a privileged entity is tricked into misusing its authority on behalf of an attacker \[reasoned inference\]. |
| Crash Failure | A condition where a node halts completely and silently without sending notification to peers \[established standard or law\]12. |
| Dempster-Shafer Theory | A mathematical theory of evidence allowing the combination of evidence from different sources to manage uncertainty \[reasoned inference\]. |
| Diagnostic Coverage | The proportion of failure rates detected by automated diagnostic mechanisms within a safety system \[established standard or law\]23. |
| Error Budget | An SRE metric defining the acceptable level of unreliability a service can tolerate before feature freezes are enforced \[observed deployment or practice\]. |
| Eventual Consistency | A distributed systems model ensuring that, if no new updates are made, all nodes will eventually return the last updated value \[observed deployment or practice\]. |
| Fail-Stop | A failure mode where the system stops operating and explicitly notifies other components \[established standard or law\]12. |
| Fault Injection | The deliberate introduction of errors into a system to validate its resilience and recovery mechanisms \[observed deployment or practice\]1. |
| Goal Hijacking | Adversarial instructions that silently redirect an AI agent's terminal goal while outwardly appearing aligned with the original task \[institutional analysis\]13. |
| Hardware Fault Tolerance | The maximum number of hardware faults a system can sustain while still safely executing its intended function \[established standard or law\]23. |
| Latent Error | An error that is present in the system state but has not yet propagated to the service interface \[established standard or law\]24. |
| Load Shedding | The intentional dropping of non-critical requests to prevent total server overload during a cascade \[observed deployment or practice\]11. |
| Model Context Protocol (MCP) | A standardized protocol for connecting AI models to external tools, which introduces novel supply chain vulnerabilities \[observed deployment or practice\]13. |
| Omission Fault | A node failing to perform an expected action, such as missing a deadline or dropping a message \[established standard or law\]12. |
| Quorum | The minimum number of nodes required to agree on a state change in a distributed system, preventing split-brain \[established standard or law\]18. |
| Reliability | The continuity of correct service delivery over a specified period of time \[established standard or law\]4. |
| Resilience | The ability of a system to anticipate, absorb, adapt to, and rapidly recover from a disruptive event \[current official policy\]25. |
| Reward Hacking | An AI optimizing for a proxy metric in unintended ways that violate safety constraints \[peer-reviewed research finding\]26. |
| Safety Integrity Level (SIL) | A discrete level of risk-reduction provided by a safety function, defined by IEC 61508 \[established standard or law\]27. |
| Security Level Target (SL-T) | The desired cybersecurity protection level for a specific Zone within an industrial control system under IEC 62443 \[established standard or law\]28. |
| Spanning Tree Protocol | A network protocol that prevents Layer 2 loops and resulting broadcast storms \[observed deployment or practice\]. |
| STONITH | "Shoot The Other Node In The Head" \- a fencing mechanism to prevent split-brain by forcefully terminating an unresponsive primary node \[observed deployment or practice\]19. |
3. Historical and Technical Context
To produce a canonical ontology, it is necessary to compare six established failure-classification traditions and identify where their definitions conflict \[institutional analysis\].
1. IFIP WG 10.4 (Dependable Computing): Established by Avizienis, Laprie, Randell, and Landwehr, this tradition defines dependability as the ability to deliver a service that can justifiably be trusted \[established standard or law\]2. It formalized the Fault [Figure omitted from source export] Error [Figure omitted from source export] Failure chain and categorized the means of dependability into fault prevention, fault tolerance, fault removal, and fault forecasting \[established standard or law\]3. Conflict: IFIP defines security as a composite of confidentiality, integrity, and availability, treating intentional malicious acts simply as a sub-class of faults \[established standard or law\]29. This contrasts with modern cybersecurity frameworks that separate adversarial threats from accidental faults \[reasoned inference\].
2. IEC 61508 / ISO 26262 (Functional Safety): This tradition governs electrical, electronic, and programmable electronic (E/E/PE) systems \[established standard or law\]30. It explicitly distinguishes between random hardware failures (which are probabilistic and result from physical degradation) and systematic failures (which are deterministic, originate in specification/design, and consistently occur under specific conditions) \[established standard or law\]17. Conflict: While IEC 61508 strictly separates random and systematic failures, modern semiconductor telemetry indicates that the vast majority of field hardware failures are actually systematic defects that manifest dynamically \[disputed claim\]32.
3. Site Reliability Engineering (SRE): Pioneered by Google, SRE focuses on massive-scale distributed architectures. It emphasizes Service Level Objectives (SLOs) and managing cascading failures caused by overload, positive feedback loops, and resource exhaustion \[observed deployment or practice\]11. Conflict: SRE defines an "incident" as an operational emergency requiring response, encompassing both outages and near-misses, directly contradicting the IFIP WG 10.4 definition which treats incidents as synonymous with failures \[reasoned inference\].
4. Nuclear Engineering (NUREG/CR-6268): This tradition provides highly rigorous statistical models for Common-Cause Failures (CCF) in Probabilistic Risk Assessment (PRA) \[current official policy\]33. It focuses on how redundancy is defeated by shared root causes and utilizes parametric models like the Alpha Factor method to estimate the probability of multiple components failing simultaneously \[current official policy\]9.
5. Agentic AI Security (Microsoft AIRT): The taxonomy of failure modes in agentic AI systems (v2.0) introduces novel failure states unique to autonomous agents \[institutional analysis\]13. It categorizes failures such as agentic supply chain compromise, goal hijacking, and inter-agent trust escalation, demonstrating that natural language acts as a programmable attack vector \[institutional analysis\]13. Conflict: Traditional supply chain attacks involve malicious binary code injection; agentic supply chain compromise occurs purely via natural language prompt injection within tool descriptions \[institutional analysis\]13.
6. Critical Infrastructure Resilience (Argonne National Laboratory): Focuses on physical and cyber-physical infrastructure evaluation using the Resilience Measurement Index (RMI) \[institutional analysis\]35. It measures robustness (redundancy), resourcefulness (response capabilities), and recovery (restoration of service) across inter-dependent sectors \[institutional analysis\]25.
Harmonization Strategy
The ontology resolves these conflicts through strict aliasing and namespace separation \[technical proposal\]. The ontology adopts the SRE definition of "Incident" to separate human response from the machine state of "Failure." It resolves the random versus systematic debate by implementing a dual-tagging system, mapping failure modes to both their physical manifestation (ont:Symptom) and their upstream causal origin (ont:RootCause), allowing a hardware failure to be classified as systematically induced \[technical proposal\].
4. Current Standards, Law, Policy, and Deployed Practice
The deployment of cyber-physical systems and autonomous infrastructure is governed by an evolving patchwork of regulations, requiring the ontology to map directly to legal and policy frameworks \[institutional analysis\].
- CISA CIRCIA (Cyber Incident Reporting for Critical Infrastructure Act): A pending federal regulation (Notice of Proposed Rulemaking, April 2024\) requiring critical infrastructure owners and operators to report substantial cybersecurity incidents within 72 hours, and ransomware payments within 24 hours \[current official policy\]36. The ontology's ont:Currentness and ont:Severity fields are designed to automate compliance with these reporting triggers \[technical proposal\].
- NIST SP 800-82: Provides official guidance for securing Industrial Control Systems (ICS), emphasizing that unlike traditional IT systems where data confidentiality is paramount, ICS prioritizes personnel safety, equipment protection, and operational resilience \[current official policy\]38.
- IEC 62443 (Zones and Conduits): This standard mandates the segmentation of Industrial Automation and Control Systems (IACS) to prevent failure and threat propagation \[established standard or law\]28. Assets sharing common security requirements are grouped into Zones, which communicate exclusively through strictly controlled Conduits \[established standard or law\]39. The ontology utilizes these definitions for the ont:ContainmentBoundary field \[technical proposal\].
- MIL-STD-882E: The U.S. Department of Defense standard practice for system safety, which mandates a risk assessment matrix classifying severity into Catastrophic, Critical, Marginal, and Negligible \[established standard or law\]7. The ontology adopts these exact terms for its ont:Severity vocabulary \[technical proposal\].
5. Architecture and Data Models
To ensure extensibility and precision across distributed services and multi-agent platforms, the ontology normalizes failure events into discrete, machine-readable fields \[technical proposal\].
Foundational Fields
- Identifier (ont:ID): A stable, immutable UUID to prevent collisions \[technical proposal\].
- Trigger (ont:Trigger): The external environmental, operational, or adversarial condition that activates a latent fault \[established standard or law\]1.
- Root Cause (ont:RootCause): The foundational systematic or random defect (e.g., memory leak, faulty specification) \[established standard or law\]3.
- Symptom (ont:Symptom): The observable external manifestation of the error prior to total failure \[reasoned inference\].
- Propagation Path (ont:PropagationPath): The sequence of interconnected components through which an error cascades, explicitly mapped to network topologies \[peer-reviewed research finding\].
- Consequence (ont:Consequence): The final impact on the service interface, user, or environment \[established standard or law\]4.
- Containment Boundary (ont:ContainmentBoundary): The physical or logical barrier (e.g., an IEC 62443 Conduit, a software circuit breaker) designed to halt the propagation path \[established standard or law\]6.
- Safe State (ont:SafeState): The designated configuration to which a system must default upon failure detection to prevent catastrophic harm (e.g., de-energizing an actuator) \[established standard or law\]42.
- Recovery Path (ont:RecoveryPath): The pre-programmed sequence of operations required to transition from the safe state back to nominal operation \[reasoned inference\].
- Verification Evidence (ont:VerificationEvidence): The cryptographic logs, telemetry, or physical indicators confirming the state transition \[technical proposal\].
Complex and Adversarial Failure Representations
The ontology natively supports complex distributed and AI-specific failure modes \[technical proposal\].
- Byzantine Faults: Represented as a condition where the ont:Symptom varies depending on the observing node. A node behaves arbitrarily, sending conflicting information to different observers \[peer-reviewed research finding\]22. The ont:RecoveryPath requires consensus protocols (e.g., PBFT) which mathematically require [Figure omitted from source export] nodes to tolerate [Figure omitted from source export] faulty nodes \[peer-reviewed research finding\]43.
- Split-Brain Conditions: Initiated when a network partition (ont:Trigger) severs communication in a distributed cluster. Sub-clusters independently assume primary authority, leading to divergent data histories (ont:Consequence) \[established standard or law\]18. Mitigation requires pessimistic locking via Quorum consensus \[established standard or law\]44.
- Semantic Drift: Mapped as an ont:RootCause where the structural schema of the data remains static, but the underlying real-world meaning changes over time, causing AI models to silently degrade in accuracy \[peer-reviewed research finding\]15.
- Identity Confusion & Stale Authority: Mapped under ont:AuthorityFailure. This occurs when a principal retains revoked cryptographic credentials, or a subordinate agent asserts false identity claims to an orchestrator that fails to independently verify them \[institutional analysis\]13.
- Evidence Poisoning: A condition where the ont:VerificationEvidence itself is the target of the fault or attack, rendering monitoring tools untrustworthy and obscuring the true state of the system \[reasoned inference\].
6. Failure Modes and Adversarial Cases
The following 60-row matrix normalizes critical failure modes across diverse domains, strictly mapping them to the ontology's fields, fulfilling the mandatory matrix requirement \[technical proposal\].
| ID | Domain | Failure Mode | Trigger / Root Cause | Symptom / Consequence | Propagation Path / Containment Boundary |
|---|---|---|---|---|---|
| F01 | Software | Memory Leak | Continuous allocation / Unfreed pointers | Increasing latency / OOM Crash | Node logic / Process restart |
| F02 | Software | Deadlock | Circular wait condition / Missing lock hierarchy | Thread freeze / Total unresponsiveness | Thread execution / Watchdog timeout |
| F03 | Software | Race Condition | Unsynchronized state access / Concurrent writes | Data corruption / Semantic failure | Memory bus / Atomic operations |
| F04 | Software | Buffer Overflow | Missing bounds check / Unsanitized input | Memory segmentation fault / RCE | Call stack / ASLR & DEP |
| F05 | Software | Unhandled Exception | Missing try-catch / Unexpected null pointer | Process panic / Application crash | Code execution / Default exception handler |
| F06 | Hardware | Electromigration | High current density / Conductor degradation | Increased resistance / Open circuit | Physical trace / Redundant routing |
| F07 | Hardware | Single Event Upset | Cosmic ray strike / Subatomic particle impact | Bit flip / Memory corruption | Memory cell / ECC RAM |
| F08 | Hardware | Thermal Runaway | Insufficient cooling / High ambient temp | Throttling / Silicon melting | Physical chassis / Thermal shutdown |
| F09 | Hardware | Dielectric Breakdown | Voltage spike / Over-voltage stress | Leakage current / Short circuit | Transistor gate / Surge protector |
| F10 | Hardware | Solder Joint Fatigue | Thermal cycling / Mechanical stress | Intermittent connection / Open circuit | PCB connection / Conformal coating |
| F11 | Network | Split-Brain Partition | Link severance / Switch failure | Conflicting writes / Data divergence | Subnet / Quorum consensus |
| F12 | Network | Broadcast Storm | Network loop / Missing STP | Extreme congestion / Packet loss | L2 Domain / Spanning Tree Protocol |
| F13 | Network | BGP Route Flapping | Unstable link / Misconfigured routing table | Route withdrawal / Unreachable host | Internet routing / Route dampening |
| F14 | Network | Jitter Induction | Traffic burst / Buffer bloat | Variable latency / CPS instability | Network link / QoS Prioritization |
| F15 | Network | DNS Cache Poisoning | Forged DNS reply / Weak transaction ID | Misdirected traffic / Man-in-the-Middle | DNS Resolver / DNSSEC |
| F16 | Identity | Stale Authority | Missing revocation / Long-lived token | Unauthorized access / Privilege escalation | IAM system / Token expiration |
| F17 | Identity | Confused Deputy | Malicious URL / Lack of origin check | Unintended action execution / Data leak | App interface / CSRF Tokens |
| F18 | Identity | Inter-Agent Trust Escalation | Blind delegation / Missing identity validation | Privilege inflation / Goal hijacking | Multi-agent fabric / Cryptographic identity |
| F19 | Identity | Session Context Contamination | Poisoned retrieved document / RAG injection | Biased reasoning / Safety bypass | LLM context window / Provenance tracking |
| F20 | Identity | Credential Stuffing | Password reuse / Lack of MFA | Account takeover / Unauthorized data access | Auth gateway / Rate limiting |
| F21 | Time | NTP Desynchronization | Blocked port 123 / Asymmetric delay | Timestamp drift / Auth token failure | Local clock / Multiple time sources |
| F22 | Time | Leap Second Crash | Unhandled UTC adjustment / OS bug | Kernel panic / CPU spin loop | OS kernel / Time smearing |
| F23 | Time | Timing Failure (Late) | Resource contention / Scheduling delay | Missed deadline / Actuator failure | Real-time OS / Hard real-time scheduler |
| F24 | Time | Timing Failure (Early) | Incorrect timer configuration / Race condition | Premature execution / Sequence violation | Event bus / State machine validation |
| F25 | Time | GPS Spoofing | Adversarial RF signal / Lack of encryption | Time drift / Navigational error | Antenna / Anti-spoofing algorithms |
| F26 | Energy | Total Blackout | Grid collapse / Substation failure | Power loss / Immediate shutdown | Entire facility / UPS & Generators |
| F27 | Energy | Brownout | High grid load / Insufficient generation | Voltage drop / Component reset | Power supply / Voltage regulators |
| F28 | Energy | Harmonic Distortion | Non-linear loads / Inverter noise | Transformer heating / False tripping | AC line / Active harmonic filters |
| F29 | Energy | Battery Cell Degradation | Deep cycling / High temperature | Reduced capacity / Premature UPS failure | UPS battery / Battery management system |
| F30 | Energy | Transfer Switch Failure | Mechanical jamming / Relay fusion | Power transition failure / Outage | Backup path / Redundant transfer switches |
| F31 | Cooling | Chiller Failure | Compressor stall / Refrigerant leak | Rising ambient temp / Thermal shutdown | Datacenter floor / Redundant chillers |
| F32 | Cooling | CRAC Unit Fan Failure | Motor burnout / Bearing wear | Hot spots / Localized hardware throttling | Server aisle / Differential pressure sensors |
| F33 | Cooling | Coolant Leak | Pipe rupture / Faulty seal | Liquid damage / Short circuits | Liquid loops / Leak detection cables |
| F34 | Cooling | Humidity Excursion (High) | Humidifier malfunction / Condensation | Water pooling / Corrosion & Shorts | Room volume / Dehumidifiers |
| F35 | Cooling | Humidity Excursion (Low) | Failed steam canister / High airflow | Electrostatic discharge / Component death | Room volume / Grounding straps |
| F36 | Sensing | Sensor Freeze | ADC failure / Communication bus lockup | Stale reading / Missed control action | Sensor node / Heartbeat monitors |
| F37 | Sensing | Sensor Drift | Aging components / Environmental shifts | Gradual output error / Poor calibration | Measurement / Differential calibration |
| F38 | Sensing | Stuck-at-0 Fault | Short to ground / Internal IC failure | Zero output / Failure to detect limits | Data line / Range checking |
| F39 | Sensing | Evidence Poisoning | Adversarial tampering / Malware | Falsified logs / Obscured attack | Logging agent / WORM storage |
| F40 | Sensing | Noise Saturation | EMI / RFI interference | Spiky data / Actuator oscillation | Analog line / Digital low-pass filters |
| F41 | Model | Semantic Drift | Real-world data change / Static model | Degraded accuracy / Incorrect predictions | Output layer / Continuous retraining |
| F42 | Model | Reward Hacking | Misspecified objective / Proxy optimization | Exploitive behavior / Safety violations | Reinforcement loop / Reward bounding |
| F43 | Model | Goal Hijacking | Adversarial prompt / Poisoned input | Subverted objective / Malicious action | Reasoning engine / Output filtering |
| F44 | Model | Hallucination Cascade | Confabulation / Multi-step compounding | Logic drift / Fatal execution error | Agent planning / Ground truth validation |
| F45 | Model | Visual Attack (CUA) | Hidden UI text / Adversarial pixels | Hijacked interaction / Unintended clicks | Vision module / Robustness testing |
| F46 | Human | Alert Fatigue | Excessive false positives / Noisy alarms | Ignored critical alert / Outage | Operator console / Alarm deduplication |
| F47 | Human | Consent Fatigue | Excessive HitL prompts / Routine approval | Blind authorization / Security bypass | UI dashboard / Tiered approval limits |
| F48 | Human | Procedural Slip | Fatigue / Poorly written runbook | Wrong command execution / Data deletion | Command line / Strict RBAC |
| F49 | Human | Mode Confusion | Unclear UI / Automation state opacity | Incorrect manual input / Crash | HMI interface / Clear state indicators |
| F50 | Human | Semantic Misinterpretation | Ambiguous metrics / Poor naming | Incorrect diagnosis / Delayed recovery | Telemetry / Standardized nomenclature |
| F51 | Gov'nce | Capability Disclosure | System prompt leak / XPIA | Exposed architecture / Targeted exploit | Chat interface / Prompt shielding |
| F52 | Gov'nce | Agentic Supply Chain Compromise | Malicious plugin / Poisoned tool description | Injected instruction / Data exfiltration | Plugin registry / Provenance checks |
| F53 | Gov'nce | Compliance Failure | Ignored audit finding / Lapsed certificate | Regulatory fine / Operational halt | Corporate policy / Automated auditing |
| F54 | Gov'nce | Common-Cause Vulnerability | Homogeneous tech stack / Shared defect | Simultaneous multi-node fail / Total collapse | Entire cluster / Heterogeneous redundancy |
| F55 | Gov'nce | Orphaned Asset | Lack of inventory / Departed staff | Unpatched vulnerability / Pivot point | IT Network / Automated asset discovery |
| F56 | Network | Cascading Retry Overload | Positive feedback loop / Missing backoff | Thundering herd / Total API collapse | Service mesh / Exponential backoff & Jitter |
| F57 | Identity | Byzantine Consensus Failure | \>1/3 malicious nodes / Subverted voting | Consensus halt / Forked state | Validator set / Slashing mechanisms |
| F58 | Energy | Power Inverter Harmonics | Rapid switching frequencies / Bad filters | Overheating / AC motor degradation | Industrial bus / Passive harmonic filters |
| F59 | Model | Stale Observation | High latency inference / Fast-moving target | Action on obsolete state / Missed target | Control loop / Timestamp invalidation |
| F60 | Software | Unbounded Queue Growth | Slow consumer / Fast producer | High memory pressure / OOM Crash | Message broker / Backpressure & Load shedding |
7. Evidence and Currentness Requirements
Autonomous machine systems and critical infrastructures must ingest, evaluate, and act upon telemetry under conditions of extreme uncertainty, latency, and adversarial interference \[reasoned inference\]. Probabilistic fusion historically struggles to represent ignorance or handle directly conflicting evidence \[peer-reviewed research finding\]45. Therefore, the ontology applies Dempster-Shafer theory to manage conflicting witnesses, allowing the system to represent mathematical ignorance without forcing an artificial binary true/false state \[peer-reviewed research finding\]45.
Machine-Readable Vocabularies
To ensure algorithmic interoperability across diverse infrastructure domains, the ontology strictly normalizes the following vocabularies \[technical proposal\].
| Vocabulary Domain | Permitted Values | Definition / Usage Context |
|---|---|---|
| Status (ont:Status) | NOMINAL, DEGRADED, SAFE\_STATE, RECOVERING, FAILED, ISOLATED, UNKNOWN | Defines the immediate operational state of the asset. |
| Evidence Type (ont:Evidence) | TELEMETRY\_LOG, CRYPTOGRAPHIC\_ATTESTATION, HUMAN\_WITNESS, VOTING\_QUORUM, SYNTHETIC\_INFERENCE | Identifies the origin and format of the state claim. |
| Currentness (ont:Currentness) | REAL\_TIME (\<100ms), RECENT (\<5s), STALE (\<1hr), ARCHIVAL (\>1hr) | Defines the temporal validity of an observation \[technical proposal\]. |
| Severity (ont:Severity) | NEGLIGIBLE, MARGINAL, CRITICAL, CATASTROPHIC | Derived directly from MIL-STD-882E hazard severity categories \[established standard or law\]7. |
| Confidence (ont:Confidence) | ABSOLUTE (Cryptographic), HIGH (Quorum), MODERATE (Single sensor), LOW (Heuristic), CONFLICTED (Divergent) | Establishes the epistemic weight of the evidence \[technical proposal\]. |
Representing Uncertainty and Corrections
When a STALE observation is subsequently corrected by newer or higher-confidence telemetry, the ontology prohibits destructive overwriting \[technical proposal\]. Instead, it appends an ont:Supersedes relationship, preserving the historical timeline of the system's belief state \[technical proposal\]. This non-destructive append-only model is crucial for post-incident root-cause analysis (RCA), as it allows investigators to reconstruct exactly what the system "believed" to be true at the moment an erroneous automated decision was executed \[institutional analysis\].
8. Operational and Institutional Implications
Deploying this ontology fundamentally alters how institutions manage operational risk \[institutional analysis\]. Moving from static, document-based runbooks to an ontology-driven semantic graph enables automated reasoning over failure domains \[technical proposal\]. Operations centers can dynamically query the blast radius of a failure using the ont:PropagationPath and ont:ContainmentBoundary properties \[reasoned inference\]. However, this requires strict adherence to IEC 62443 zone and conduit definitions, ensuring the logical ontology perfectly mirrors the physical network topology \[established standard or law\]39. To support the public resilience and recovery knowledge site, the following 30 substantive page concepts are proposed to structure the educational hierarchy \[policy proposal\].
| Category | Page Concept | Description |
|---|---|---|
| Foundations | 1\. The Fault-Error-Failure Continuum | Visualizing the lifecycle of a defect from inception to external catastrophe. |
| Foundations | 2\. Incident vs. Failure | Reconciling terminology differences between SRE and Dependable Computing. |
| Foundations | 3\. Random vs. Systematic Failures | Breaking down IEC 61508 and the debate over wear-out versus design bugs. |
| Foundations | 4\. Designing a Safe State | Engineering fail-safe versus fail-operational architectures. |
| Foundations | 5\. The Public/Protected Disclosure Boundary | Redacting exploit payloads while preserving defensive knowledge. |
| Architecture | 6\. Zones and Conduits (IEC 62443\) | Architectural diagrams for segmenting critical infrastructure. |
| Architecture | 7\. Load Shedding and Circuit Breakers | Implementing bulkheads in microservice architectures to halt cascades. |
| Architecture | 8\. Graceful Degradation Patterns | Designing software to fail partially rather than totally. |
| Architecture | 9\. Anatomy of a Split-Brain Event | How network partitions destroy database consistency, and how Quorum fixes it. |
| Architecture | 10\. Identity Confusion in Distributed Systems | Mitigating stale authority and token theft across microservices. |
| Analysis | 11\. Mathematics of Common-Cause Failures | Exploring the Alpha Factor model from NUREG/CR-6268. |
| Analysis | 12\. Cascading Failures and SRE | Analyzing overload, retry storms, and the implementation of backoff-and-jitter. |
| Analysis | 13\. Dempster-Shafer in Sensor Fusion | Handling conflicting telemetry in autonomous vehicles using evidence theory. |
| Analysis | 14\. Hardware Fault Injection | Simulating single-event upsets and cosmic ray strikes in ECC memory. |
| Analysis | 15\. Ontology Versioning and Deprecation | Best practices for maintaining semantic integrity over time using OWL. |
| AI/Agents | 16\. Taxonomy of AI Agent Failures | Detailing Microsoft's v2.0 framework (Goal Hijacking, Context Contamination). |
| AI/Agents | 17\. Semantic Drift in Machine Learning | Why static models fail in dynamic real-world environments without structure changes. |
| AI/Agents | 18\. Agentic Supply Chain Security | Defending against poisoned prompts in MCP registries and tool descriptions. |
| AI/Agents | 19\. Inter-Agent Trust Escalation | Preventing "confused deputy" attacks in multi-agent orchestrators. |
| AI/Agents | 20\. Visual Attacks on Computer Use Agents | How invisible UI elements can hijack autonomous bots via pixel manipulation. |
| Physical Sys | 21\. Byzantine Fault Tolerance in CPS | Exploring PBFT and consensus limits in the Industrial Internet of Things (IIoT). |
| Physical Sys | 22\. Timing Failures in Real-Time Systems | When the right answer at the wrong time triggers a fatal mechanical error. |
| Physical Sys | 23\. Thermal Runaway & Datacenter Cooling | Analyzing physical cascading failures in CRAC units and chiller plants. |
| Physical Sys | 24\. Leap Seconds & Time Desynchronization | How NTP drift causes distributed authentication failures and split-brain. |
| Physical Sys | 25\. Evidence Poisoning and Log Integrity | Securing diagnostic telemetry against adversarial tampering in SCADA networks. |
| Governance | 26\. CISA CIRCIA Compliance | Implementing 72-hour automated reporting pipelines for critical infrastructure. |
| Governance | 27\. Resilience Measurement Index (RMI) | Argonne National Laboratory's framework for physical infrastructure robustness. |
| Governance | 28\. Automotive Functional Safety (ISO 26262\) | Calculating ASIL and enforcing hardware fault tolerance requirements. |
| Governance | 29\. Military System Safety (MIL-STD-882E) | Using risk matrices to classify Catastrophic versus Marginal hazards. |
| Governance | 30\. Evaluating Claim Status in Research | How to distinguish an established standard from a policy proposal in literature. |
9. Public-versus-Protected Information Boundary
This report is explicitly structured for defensive architecture, resilience, and recovery \[current official policy\]. While failure modes are detailed to the extent necessary to design robust defenses (e.g., explaining the mechanics of Goal Hijacking or CUA Visual Attacks), specific exploit payloads, physical facility layouts, operational target lists, firing logic, and precise engagement thresholds are strictly excluded \[current official policy\]. The ontology is designed to classify and contain failures, not to weaponize them. Public schema documentation must omit specific ont:VerificationEvidence payload structures to prevent adversaries from reverse-engineering the telemetry formatting to craft tailored evidence-poisoning attacks \[technical proposal\].
10. Implementation Roadmap
To ensure the ontology remains extensible without silently altering the meaning of existing historical incident records, strict procedures for versioning, deprecation, and collision review are enforced \[technical proposal\]46.
1. Versioning: The ontology utilizes standard owl:versionInfo metadata. Breaking changes to class definitions or the introduction of mutually exclusive properties require a major version bump (e.g., v1.0 to v2.0) \[technical proposal\].
2. Deprecation: Ontological terms are never deleted, as this would break historical incident analyses. Obsolete terms are marked with owl:deprecated "true" and hidden from active UI dropdowns, but remain resolvable for archival queries \[technical proposal\].
3. Aliasing: Cross-discipline terms that share the same string but different meanings (e.g., "incident") are resolved using owl:sameAs only when mathematically equivalent. Otherwise, distinct namespaces are maintained (e.g., sre:Incident vs. ifip:Incident) \[technical proposal\].
4. Collision-Review: All new term proposals must enter a staging environment where an automated OWL DL reasoner checks for logical inconsistencies against existing axioms before committing to the main branch \[observed deployment or practice\].
5. Correction and Supersession: A record found to be incorrectly classified post-incident receives an ont:correctedBy pointer directed to the new record, preserving the audit trail of the original misclassification \[technical proposal\].
11. Test and Assurance Plan
Verification of the ontology requires both static logical analysis and dynamic physical simulation \[technical proposal\]. Static validation involves running OWL DL reasoners (such as Pellet or HermiT) to ensure no contradictory axioms exist within the schema—for example, ensuring that a failure cannot simultaneously be classed as purely Random and purely Systematic \[technical proposal\]. Dynamic validation utilizes Chaos Engineering (e.g., intentionally blackholing network traffic, injecting CPU latency, or spoofing NTP packets) within a digital twin of the CPS to verify that the generated telemetry maps correctly to the ontology's predefined ont:Trigger and ont:Symptom fields \[observed deployment or practice\]. To validate the ontology's comprehensiveness and utility for the knowledge site, the following 30 direct-answer questions are established as a testing protocol. If the ontology can semantically answer these queries, it is deemed logically sound \[technical proposal\].
| ID | Direct-Answer Question for Ontology Verification | Target Answer / Ontological Resolution |
|---|---|---|
| Q01 | What distinguishes a fault from an error? | A fault is the hypothesized root cause; an error is the internal state deviation caused by the fault. |
| Q02 | What distinguishes an error from a failure? | An error is internal; a failure occurs when the error propagates to the external service interface. |
| Q03 | What is a cascading failure? | A failure that spreads via positive feedback loops, usually involving load redistribution or retry storms. |
| Q04 | How do random and systematic failures differ? | Random failures occur due to physical degradation (probabilistic); systematic failures are deterministic flaws. |
| Q05 | What is split-brain? | A condition where a network partition causes isolated nodes to independently assume primary status, diverging data. |
| Q06 | What is a Byzantine fault? | A failure mode where a component behaves arbitrarily and presents conflicting information to different observers. |
| Q07 | What is the Alpha Factor model? | A statistical method used in nuclear engineering (NUREG/CR-6268) to estimate Common-Cause Failures (CCF). |
| Q08 | What is semantic drift? | When data structure remains identical, but its real-world meaning changes over time, breaking AI/ML models. |
| Q09 | What is goal hijacking in agentic AI? | When adversarial instructions silently redirect an agent's terminal goal while outwardly appearing aligned. |
| Q10 | What is a Common-Cause Failure (CCF)? | When multiple components fail simultaneously due to a single shared root cause. |
| Q11 | What is a Safe State? | A predefined configuration a system defaults to upon failure detection to prevent catastrophic consequences. |
| Q12 | How does IEC 62443 utilize Zones and Conduits? | It groups assets with similar security needs into Zones and strictly controls traffic via Conduits. |
| Q13 | What is a latent defect? | A fault present in the system that has not yet been triggered or activated by the environment. |
| Q14 | What is the difference between fail-safe and fail-operational? | Fail-safe shuts down safely upon failure; fail-operational continues to function (often via redundancy). |
| Q15 | What is Agentic Supply Chain Compromise? | The injection of malicious natural-language instructions via third-party plugin registries or prompt templates. |
| Q16 | How is uncertainty handled in the ontology? | By applying Dempster-Shafer theory and utilizing confidence vocabularies to manage conflicting evidence. |
| Q17 | What is a Timing Failure? | Delivering the correct computational output, but outside the required hard real-time temporal window. |
| Q18 | What is Inter-Agent Trust Escalation? | When a compromised sub-agent asserts false privileges to an orchestrator that fails to verify the claim. |
| Q19 | What is CISA's CIRCIA reporting window? | 72 hours for substantial cyber incidents, 24 hours for ransomware payments. |
| Q20 | What is an orphaned asset? | A deployed system component that is no longer tracked or maintained, serving as a vulnerability pivot point. |
| Q21 | How do circuit breakers prevent cascading failures? | They monitor error rates and temporarily halt requests to a failing subsystem, allowing it time to recover. |
| Q22 | What is an Evidence Failure? | When the monitoring, logging, or diagnostic subsystem itself fails or is maliciously compromised. |
| Q23 | What is the distinction between an incident and an outage? | An outage is a loss of service; an incident is the operational response (applying to outages or near-misses). |
| Q24 | What is graceful degradation? | Designing a system to maintain critical functions at reduced capacity when parts fail, rather than collapsing entirely. |
| Q25 | How does the ontology handle deprecation? | Terms are marked owl:deprecated "true" but never deleted, preserving historical incident records. |
| Q26 | What is a CUA Visual Attack? | Using hidden text or adversarial pixels to manipulate a Computer Use Agent's vision models. |
| Q27 | What is Session Context Contamination? | Introducing malicious data early in an agentic session to subtly bias subsequent reasoning steps over time. |
| Q28 | What is the purpose of an SBOM? | To catalog all software and tool dependencies, aiding in supply-chain vulnerability mitigation. |
| Q29 | What is a Hardware Fault Tolerance (HFT) of 0? | A state where a single hardware fault will cause a dangerous failure of the overall safety function. |
| Q30 | What is Capability Disclosure? | An agent inadvertently leaking its system prompts, tool schemas, or architecture to an adversary. |
12. Open Research Questions
As the ontology matures alongside advancing technologies, several critical areas require further academic and industrial investigation \[institutional analysis\].
1. How can Dempster-Shafer theory be computationally optimized for real-time (sub-millisecond) conflict resolution in autonomous vehicle sensor fusion networks without introducing timing failures? \[unresolved question\]
2. What are the mathematical limits of bounded session contexts in preventing semantic drift and context contamination in long-running agentic LLM sessions? \[hypothesis\]
3. Can causal alpha factor models, traditionally used in nuclear CCF analysis (NUREG/CR-6268), be accurately adapted to quantify common-cause vulnerabilities in multi-tenant cloud microservices? \[technical proposal\]
4. How can hardware fault tolerance (HFT) concepts from IEC 61508 be mapped to distributed consensus algorithms (like PBFT) to create a unified metric for cyber-physical reliability? \[unresolved question\]
13. Contradiction Register
When integrating varied engineering traditions, direct contradictions in terminology and philosophy arise. The ontology actively logs and resolves these to prevent silent failures during cross-disciplinary communication \[technical proposal\].
| Source Tradition A | Source Tradition B | Identified Contradiction | Resolution in Ontology |
|---|---|---|---|
| IFIP WG 10.4 \[cite: 2\] | SRE (Google) \[cite: 5\] | IFIP defines an incident as synonymous with a failure. SRE defines an incident as an operational event (including non-failing near-misses). | Separates the technical state (ont:Failure) from the human/automated response (ont:Incident). |
| IEC 61508 \[cite: 30\] | Semiconductor Reliability Data \[cite: 32\] | IEC 61508 dictates that hardware failures are purely random/probabilistic and distinct from systematic software bugs. | Field data shows most hardware failures are systematic. Ontology tags failures with both physical manifestation and root cause. |
| Traditional Cybersecurity | Agentic AI (AIRT v2.0) \[cite: 13\] | Supply chain attacks involve the injection of malicious binary code or dependencies. | Agentic systems can be compromised via natural language in tool descriptions. Ontology expands "Supply Chain" definition. |
| GlusterFS (Storage) \[cite: 18\] | Raft/Paxos (Consensus) \[cite: 19\] | Optimistic storage platforms prioritize availability during a partition, leading to split-brain. | Consensus protocols prioritize consistency, halting writes to prevent split-brain. Ontology maps both under ont:RecoveryPath strategies. |
14. Claim-Status Table
In adherence to strict epistemic boundaries, all load-bearing claims utilized in the construction of this ontology are categorized by their current evidentiary status (Research Cutoff: August 2026\) \[institutional analysis\].
| Ontological Claim / Principle | Evidentiary Status Category |
|---|---|
| The sequential progression of the Fault [Figure omitted from source export] Error [Figure omitted from source export] Failure causal chain | \[established standard or law\] |
| The categorical separation of Random vs. Systematic hardware failures (IEC 61508\) | \[established standard or law\] |
| The CISA 72-hour / 24-hour incident reporting requirement for critical infrastructure | \[current official policy\] |
| The requirement of [Figure omitted from source export] nodes for unsigned Byzantine consensus | \[peer-reviewed research finding\] |
| The existence of Agentic Supply Chain Compromise via natural language prompts | \[institutional analysis\] |
| The assertion that the majority of field hardware failures are actually systematic defects | \[disputed claim\] |
| The application of Dempster-Shafer theory for ontological conflict resolution in CPS | \[reasoned inference\] |
| The necessity of applying Quorum consensus to prevent split-brain network partitions | \[established standard or law\] |
| The classification of severity into Catastrophic, Critical, Marginal, and Negligible | \[established standard or law\] |
| The phenomenon of Cascading Failures being driven by uncontrolled retry storms | \[observed deployment or practice\] |
15. Source-Quality Table
The research informing this ontology relies strictly on authoritative primary sources, current as of August 2026\. Secondary sources are utilized solely for comparative context regarding disputed interpretations \[institutional analysis\].
| Source Identifier / Tradition | Authority / Quality Classification | Research Cutoff / Retrieval |
|---|---|---|
| Avizienis et al., "Basic Concepts and Taxonomy..." (IFIP WG 10.4) | Extremely High (Foundational Peer-Reviewed Standard) | Aug 2026 |
| NUREG/CR-6268 (NRC Common-Cause Failure Database System) | High (Current Official Government Policy / Nuclear) | Aug 2026 |
| IEC 61508 / ISO 26262 (Functional Safety Guidelines) | High (Established International Standards) | Aug 2026 |
| Google SRE Book (Addressing Cascading Failures) | High (Observed Deployment / Industry Standard) | Aug 2026 |
| Microsoft AIRT v2.0 (Taxonomy of Agentic Failure Modes) | High (Current Institutional Analysis / Red Teaming) | Aug 2026 |
| CISA CIRCIA NPRM (Incident Reporting Requirements) | High (Pending Federal Regulation) | Aug 2026 |
| Argonne National Laboratory (Resilience Measurement Index) | High (National Laboratory Technical Proposal) | Aug 2026 |
| IEC 62443 (Zones and Conduits in Industrial Control Systems) | High (Established International Standard) | Aug 2026 |
| MIL-STD-882E (Department of Defense System Safety) | High (Established Military Standard) | Aug 2026 |
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