This page describes a **reference architecture** for a controlled, authorized password cracking system used in security testing and research. This is a **SYSTEM**, if you are looking for guidance on how to build a single node or machine, you can find that in this [[Password Cracking Machine Recipe|article]]. The goal of this architecture is to: - Support **repeatable, measurable experiments** - Maintain **strict separation** between cracking infrastructure and production systems - Enforce **authorization, auditing, and data protection** - Make it easy to reason about **risk and impact** This is a conceptual model intended to inform the development and construction of a system, it is not a prescriptive hardware or tooling design. --- ## Design Goals A defensible password cracking system should be designed around the following goals: - **Isolation** Cracking workloads must be kept separate from production environments and sensitive internal networks. _Example:_ Password cracking jobs are executed on dedicated, non-domain-joined systems or isolated cloud instances with no trust relationship to production Active Directory, preventing a compromised cracking host from becoming an attack pivot. - **Control and governance** Access, usage, and data flows must be tightly controlled and auditable. _Example:_ Only approved security personnel can submit hash datasets, all cracking jobs are logged, access is role-based, and results are stored in controlled repositories with clear retention and deletion policies to support audit and compliance requirements. - **Repeatability** Experiments should be reproducible across time, hardware, and personnel. _Example:_ The same hash dataset, wordlists, rulesets, and attack order can be rerun months later to validate results or compare policy changes, ensuring findings are defensible and not dependent on a single analyst’s custom setup. - **Scalability** The system should be able to scale up or down without architectural changes. _Example:_ A cracking workflow can run on a single CPU for small assessments or scale out to multiple GPU-backed workers for larger engagements, without changing tooling, data formats, or analysis methodology. - **Data protection** Hashes, associated metadata, and any derived results must be handled as sensitive data. _Example:_ Hashes and cracked passwords are encrypted at rest, transmitted securely, access is limited on a need-to-know basis, and outputs are sanitized or summarized when shared with stakeholders to avoid unnecessary exposure of plaintext credentials. --- ## High-Level Architecture Overview At a high level, a password cracking system can be thought of as five cooperating layers: 1. **Management and orchestration** 2. **Compute nodes (cracking workers)** 3. **Storage and data management** 4. **Monitoring, logging, and observability** 5. **Access control and governance** These components are typically deployed within a **segmented network zone** dedicated to security testing or a stand alone capability that is physically separated from other environements. --- ## 1. Management and Orchestration The management layer coordinates all activity in the system. Typical responsibilities: - Job definition and scheduling - Distribution of workloads to compute nodes - Tracking job status, progress, and outcomes - Enforcing policies (e.g., allowed hash types, time limits, quotas) Key architectural considerations: - Exposed via a **restricted administrative interface** (VPN, bastion, or jump host) - Every action should be **authenticated, authorized, and logged** - API access should be **keyed and scoped** to specific use cases --- ## 2. Compute Nodes (Cracking Workers) Compute nodes perform the actual cracking workloads. Characteristics: - May be GPU-optimized, CPU-heavy, or a mix of both - Often run homogeneous, hardened base images - Receive **task definitions and input data** from the management layer - Return **results and metrics** rather than full datasets whenever possible Architectural principles: - Treat nodes as **stateless workers** where possible - Do not store long-term data or secrets on worker nodes - Use **configuration management** or templates for consistent builds - Support **horizontal scaling** (add or remove nodes without redesign) --- ## 3. Storage and Data Management Storage is responsible for safely handling: - Hash sets and related metadata - Wordlists, rules, and configuration artifacts - Experiment definitions and results - Logs and audit data Best practices: - Separate **operational storage** (for running jobs) from **archival storage** (for long-term results) - Encrypt sensitive data at rest - Implement **access controls** at the storage layer (not just at the application layer) - Define clear **data retention policies** and deletion procedures --- ## 4. Monitoring, Logging, and Observability For the system to be defensible, it must be observable. Monitor and log: - Job submissions, modifications, and cancellations - Authentication and authorization events - System health (CPU/GPU utilization, memory, disk, network) - Errors, timeouts, and anomalous behavior Key properties: - Logs are shipped to a **centralized, tamper-resistant store** - Access to logs is controlled and auditable - Metrics can be used to **validate experiments** and support capacity planning --- ## 5. Access Control and Governance The governance layer defines **who can do what, under which conditions**. Typical controls: - Strong authentication (MFA, hardware tokens, or equivalent) - Role-based access control (RBAC) for: - System administrators - Security testers - Researchers - Read-only observers - Change management and approval workflows for: - New datasets - New policies - Major configuration changes Governance should also cover: - **Acceptable use policies** - **Legal and contractual constraints** - **Documentation requirements** for each engagement or project --- ## Data Flow Lifecycle A typical data flow through the system looks like this: 1. **Ingestion** - Authorized hashes and associated metadata are imported into the system. - Scope, ownership, and purpose are documented. 2. **Preparation** - Hash types and formats are validated. - Datasets are tagged with relevant attributes (e.g., environment, project, date). 3. **Job Definition** - An experiment is defined, including: - Target dataset - Allowed techniques - Time or resource constraints - Measurement goals 4. **Assignment and Execution** - Management layer assigns jobs to compute nodes. - Workers process tasks and report progress and outcomes. 5. **Result Collection** - Results are centralized in storage, emphasizing: - Aggregate metrics - Structural or statistical findings - Minimal exposure of sensitive data 6. **Analysis and Reporting** - Results are interpreted in line with Hashtopia’s methodology. - Recommendations and findings are documented for defensive use. 7. **Archival and Cleanup** - Temporary data (intermediate files, local caches) is securely deleted. - Long-term storage follows defined retention and access policies. --- ## Security and Isolation Controls To reduce risk, the architecture incorporates: - **Network segmentation** - Cracking environment in a dedicated zone - Restricted inbound and outbound connectivity - **Hardened base images** - Minimal services - Regular patching and configuration baselines - **Secrets management** - No hard-coded credentials in images or scripts - Centralized secret store with audit trails - **Strict data handling** - No direct access to production databases from the cracking environment - Import/export processes are controlled and logged --- ## Scaling and Performance Considerations A password processing or cracking architecture must be designed to scale **operationally**, not just computationally. Scaling should be achievable by adding or removing worker capacity(such as CPU or GPU nodes) without requiring changes to core logic, workflows, or security boundaries. This allows organizations to respond flexibly to project demands, whether that means accelerating a time-sensitive assessment or conserving resources during quieter periods. To handle fluctuating workloads, systems should rely on **queues, schedulers, or job orchestration layers** that decouple task submission from execution. This prevents bursts of work from overwhelming resources and ensures that jobs are processed predictably and fairly. In larger environments, it may also be necessary to separate **interactive workloads** (such as exploratory analysis or short test runs) from **long-running batch workloads** to avoid contention and ensure responsiveness for analysts. Equally important is **performance visibility**. Tracking metrics such as guess rates, job duration, resource utilization, and failure rates allows teams to understand where bottlenecks exist and to make informed decisions about hardware upgrades, configuration changes, or model selection. These metrics also provide historical context, enabling more accurate planning and expectation-setting for future engagements. Crucially, performance optimization must never come at the expense of **security controls or governance**. Scaling should not bypass access restrictions, weaken auditability, or expose sensitive data. A well-designed system treats performance as a managed capability, one that operates within clearly defined security, isolation, and compliance boundaries rather than overriding them. --- ## Relationship to Hashtopia Methodology This architecture exists to support the methodologies described throughout Hashtopia: - **[[1. Foundational Approach to Password & Hash Analysis]]** Defines how password risk is understood conceptually. - **[[2. Cracking Methodology]]** Describes how controlled cracking experiments are structured. - **[[Password Pattern Analysis]]** Provides the behavioral and statistical lens on password data. The system architecture is the **infrastructure expression** of these principles. It enables disciplined, authorized, and measurable work. --- ## Intended Outcome A well-designed password cracking system architecture should: - Make **authorized, defensive use** of cracking techniques practical and safe - Provide a clear audit trail for all activities - Enable consistent, reproducible security research and testing - Reduce uncertainty about how cracking infrastructure is operated and governed The architecture is not about building the “fastest rig”, it is about building a system that **stands up to scrutiny**. ## Architecture Diagram ┌────────────────────────────┐ │ Management & Orchestration│ │ - Job scheduling │ │ - Policy enforcement │ │ - Experiment definitions │ └───────────────┬────────────┘ │ │ (Job definitions, configs) │ v ┌───────────────────────────────────────────────────────┐ │ Compute Layer │ │ ┌────────────────────────┐ ┌──────────────────┐ │ │ │ Worker Node (GPU) │ │ Worker Node (CPU)│ │ │ │ - Executes tasks │ │ - Executes tasks │ │ │ │ - Stateless design │ │ - Scalable │ │ │ └──────────────┬─────────┘ └──────────┬──────┘ │ │ │ │ │ │ (Results, metrics) (Results, metrics) │ └─────────────────┴────────────────────────┴────────────┘ │ v ┌──────────────────────────┐ │ Storage Layer │ │ - Hash datasets │ │ - Wordlists / rules │ │ - Experiment results │ │ - Logs & audit data │ └────────────────┬─────────┘ │ v ┌─────────────────────────────┐ │ Monitoring / Logging Layer │ │ - System health metrics │ │ - Job logs & audit trails │ │ - Error and anomaly reports │ └────────────────┬────────────┘ │ v ┌──────────────────────────────┐ │ Access Control & Governance │ │ - RBAC │ │ - MFA & identity controls │ │ - Approval workflows │ │ - Acceptable use policies │ └──────────────────────────────┘ A controlled password cracking system operates as a set of isolated, auditable components. The management layer defines and orchestrates experiments, compute nodes perform work, storage maintains sensitive artifacts, logging ensures observability, and governance controls who can access what. [[1. Concepts|Concepts]] [[Home]] #research #education