This section describes the **cracking methodology** used on Hashtopia. It explains how controlled hash-cracking experiments are used to **measure password and authentication risk**, not to exclusivly geared towards obtaining credentials. Cracking, in this context, is a **diagnostic tool**. It is used to evaluate the interaction between password choices, hashing configurations, and real-world constraints. All techniques described here assume **explicit authorization** and are intended for education, research, auditing, and defensive security assessment. --- ## Purpose of Cracking in Security Analysis Password cracking, in a defensive or analytical context, is **not about recovering secrets for their own sake**. It is a measurement technique used to evaluate how real systems behave under realistic pressure. Cracking exercises are used to answer questions such as: - **How resistant is a given hashing configuration to realistic attack effort?** By measuring how many guesses succeed within a fixed amount of time and resources, analysts can determine whether a hash algorithm, cost factor, or configuration meaningfully slows attackers, or merely creates the appearance of protection. - **How does password policy influence real-world outcomes?** Cracking results reveal how users respond to policy: whether complexity rules push them toward predictable patterns, whether rotation creates incremental drift, and whether length allowances actually translate into stronger passwords in practice. - **Which classes of passwords fail first, and why?** Early successes expose where probability mass is concentrated. This allows analysts to identify dominant structures, common roots, reuse patterns, and lifecycle behaviors that theoretical models or policy documents cannot capture. - **How do salts, iterations, or algorithm choice affect feasibility?** Comparing results across different hashing schemes shows how defensive controls interact with attacker economics, hardware capabilities, and time horizons, highlighting which protections meaningfully change outcomes and which do not. In this context, the recovered passwords are **incidental artifacts**, not the objective. The true value lies in the **measured outcomes**: crack rates over time, pattern prevalence, diminishing returns, and behavioral signals that inform risk assessment, policy design, and remediation priorities. Hash cracking, when used correctly, functions as an empirical lens into password security, not a goal, but a diagnostic tool for understanding where systems fail and why. --- ## Foundational Principles The cracking methodology on Hashtopia follows these principles: - **Authorization is mandatory** Only analyze hashes from systems you own or are explicitly permitted to test. - **Measurement over extraction** Focus on rates, patterns, and distributions, not individual credentials. - **Controlled experimentation** Each experiment should vary a limited number of variables to support meaningful conclusions. - **Reproducibility** Results must be repeatable given the same inputs, tools, and parameters. - **Defensive interpretation** Findings should lead to recommendations that reduce risk. --- ## Conceptual Cracking Model Cracking experiments are treated as **controlled simulations** of pressure applied to an authentication system. Inputs include: - Hash algorithm and parameters - Salting and iteration strategy - Password characteristics - Resource constraints (time, hardware) Outputs are evaluated in terms of: - Success rates over time - Password categories affected - Computational cost - Diminishing returns --- ## General Experiment Structure While implementations vary, most password cracking experiments follow a consistent structure. The goal is not simply to recover passwords, but to **generate defensible insight** about system behavior, user behavior, and security design. --- ### 1. Define the Objective Every cracking experiment must begin with a clearly defined objective. This determines what data is collected, how results are interpreted, and what conclusions are valid. Examples of well-defined objectives include: - Comparing resistance between two hash configurations (e.g., NTLM vs PBKDF2) - Measuring how minimum length requirements affect time-to-compromise - Evaluating how quickly common password classes fail under realistic pressure **Example:** An organization wants to decide whether increasing PBKDF2 iterations from 100,000 to 300,000 materially improves security. The objective is _not_ to crack as many passwords as possible, but to measure the **difference in effort required** between the two configurations. Without a clear objective, recovered passwords can appear meaningful while actually answering no useful question. --- ### 2. Identify and Document Inputs All relevant inputs must be identified and documented before execution. These define the scope and validity of the experiment. Typical inputs include: - Hash algorithm and encoding format - Whether salts are present and how they are generated - Iteration counts or memory-cost parameters - Dataset size, origin, and characteristics **Example:** If testing password policy effectiveness, it matters whether the dataset comes from employees, contractors, or test accounts, and whether passwords were user-chosen or system-generated. Undocumented assumptions, such as treating all hashes as equivalent or ignoring salting behavior, can invalidate conclusions. --- ### 3. Select an Experimental Strategy The strategy should be chosen based on the **question being asked**, not on convenience or tool familiarity. Common strategies include: - Baseline testing to establish a starting point - Incremental pressure (adding rules, masks, or models over time) - Comparative testing across multiple configurations or datasets **Example:** To understand guessability, an analyst might apply dictionary attacks first, then rules, then structure-aware models, observing _when_ passwords fail rather than _whether_ they fail eventually. The objective is insight into **failure dynamics**, not maximum password recovery. --- ### 4. Execute in a Controlled Environment Execution should take place in a controlled, isolated environment to ensure results are reliable and reproducible. Key considerations include: - Using non-production systems only - Tracking hardware, runtime, and resource constraints - Logging configurations, parameters, and timestamps - Avoiding mid-experiment changes without documentation **Example:** If GPU count or clock speed changes mid-run, observed performance differences may reflect hardware drift rather than password strength. Uncontrolled environments produce results that cannot be compared or defended. --- ### 5. Measure and Record Outcomes Measurement focuses on _how_ and _when_ failures occur, not just on totals. Relevant metrics may include: - Percentage of hashes compromised over time - Which password classes fail first - Effort required to reach diminishing returns - Cost per recovered credential **Example:** An experiment might show that 60% of passwords fall within the first 10 million guesses, while the remaining 40% require exponentially more effort. This reveals **risk concentration**, not just success rate. Raw results should always be preserved to allow reanalysis or independent validation. --- ### 6. Interpret Results Defensively Interpretation should connect observed outcomes back to design and behavior, not celebrate recovered passwords. Key interpretive questions include: - Which system choices most affected resistance? - What user behaviors dominated failure patterns? - How would changes to policy, hashing, or tooling alter outcomes? **Example:** If most cracked passwords follow predictable lifecycle patterns (e.g., base word + year), the takeaway is not “users choose weak passwords,” but that **policy and memorability pressure shape predictable behavior**. --- ## What This Methodology Avoids This cracking methodology intentionally avoids: - “Success-only” reporting - Tool or hardware comparison without context - Publishing recovered credentials - Implicit encouragement of unauthorized activity - Framing cracking as a benchmark of skill Cracking without context creates misleading conclusions and unnecessary risk. The Hashtopia methodology is designed to aid you in building deep analysis processes and reveal the various data points that you can correlate to defeat weak cryptographic implementatoins. --- ## Ethical and Legal Considerations - Never crack hashes without explicit permission - Never publish raw passwords or identifiable credential data - Sanitize and aggregate results when sharing findings - Follow responsible disclosure practices where applicable --- ## Relationship to Other Methodology Sections - **[[1. Foundational Approach to Password & Hash Analysis]]** Defines how to think about password risk before using any tools - **[[3. General Methodology]]** Provides workflow structure, documentation standards, and verification steps - **[[Password Pattern Analysis]]** Complements cracking experiments with statistical and pattern-based analysis --- ## Intended Outcome After understanding this methodology, readers should: - View cracking as a measurement technique, not an objective - Understand how system design choices shape outcomes - Be able to interpret cracking results defensibly and responsibly