Security Debt in Software Systems

• Security Debt is the accumulation of unaddressed security vulnerabilities, misconfigurations, and design flaws arising from postponed security tasks.
• It forms throughout the SDLC due to tight deadlines, legacy constraints, and the prioritization of functionality over security.
• The resulting risks increase the attack surface and remediation costs over time, necessitating proactive governance and effective metrics.

Security Debts (SDs) are the accumulated, unresolved security weaknesses in a software system—vulnerabilities, misconfigurations, missing controls, problematic design decisions, and poorly enforced policies—that remain unaddressed because security work has been postponed, deprioritized, or inadequately specified. In the contemporary software literature, SD is treated as a specific subset of technical debt: it arises when short-term constraints such as deadlines, resource limits, legacy constraints, or weak security awareness lead teams to defer security-related work, thereby increasing exposure of assets, users, and organizations over time (Boufaied et al., 15 Jul 2025). Related work extends this idea from general software systems to blockchain architectures, smart contracts, and self-admitted technical debt (SATD), where developers explicitly document insecure shortcuts or unresolved weaknesses in comments, issues, commits, and pull requests (Ahmadjee et al., 2019).

1. Conceptual foundations and definitional boundaries

The core software-engineering definition treats Security Debt as unresolved or unaddressed security vulnerabilities and as “a specific subset of technical debt that arises from postponed security-related tasks during software development” (Boufaied et al., 15 Jul 2025). Two practitioner perspectives coexist. One treats SD as the insecure state of the system itself: latent flaws or exploitable weaknesses that enable unauthorized access, impersonation, data leaks, tampering, or service disruption. The other emphasizes intentionality: vulnerabilities or problems that are already detected but still unresolved because of deadlines, client pressure, low prioritization, or other organizational trade-offs. The literature also recognizes unintentional SD, especially when debt originates from lack of awareness or inadequate expertise rather than explicit risk acceptance (Boufaied et al., 15 Jul 2025).

A key boundary condition is that not every vulnerability is necessarily debt. Practitioners distinguish actionable gaps from unavoidable exposure: a vulnerability with no known fix or no actionable mitigation is generally not regarded as debt, whereas a known missing control, known design flaw, or known vulnerable configuration that could be addressed but has been deferred is treated as SD (Boufaied et al., 15 Jul 2025). This distinction matters because the debt metaphor presupposes an obligation that can, in principle, be repaid.

The relationship to technical debt is direct but non-identical. Technical debt covers suboptimal technical decisions made for short-term benefit; Security Debt is the subset of those decisions or omissions that undermine security goals and directly affect confidentiality, integrity, and availability. Blockchain-oriented work makes the same distinction in architectural terms: ill-informed or sub-optimal choices regarding blockchain type, storage, consensus, cryptography, key management, and smart contracts are treated as security technical debt because they create future security costs and risks even if they temporarily simplify development or deployment (Ahmadjee et al., 2019).

A practical characterization proposed in practitioner-oriented work models SD as the set of recognized vulnerabilities that are not fixed:

$SD = R \setminus F$

where $R$ is the set of recognized or identified vulnerabilities and $F$ is the subset that has been fixed (Boufaied et al., 15 Jul 2025). This suggests a risk-centric rather than purely syntactic view: SD includes not only known bugs, but also known missing controls such as absent MFA, absent encryption, weak segregation of duties, overly broad permissions, or fragile authentication flows.

2. How Security Debt forms across the SDLC

Empirical evidence from interviews with 22 practitioners indicates that the dominant drivers of SD are tight deadlines and the prioritization of functionality and client requirements over security, rather than a simple lack of tools (Boufaied et al., 15 Jul 2025). Additional recurring causes include poor or missing security requirements and documentation, lack of security awareness and expertise, organizational cultures that frame security as a blocker, legacy systems and infrastructure constraints, lack of supervision or review for junior staff, and operational complexity such as misconfigured cloud roles or complicated access-control policies. The same study reports that teams often trade security work for delivery speed, although some organizations maintain a more consistently security-first stance (Boufaied et al., 15 Jul 2025).

Security Debt accumulates across all SDLC stages. In requirements engineering it appears as missing identification of critical assets, missing threat scenarios, and absent requirements for encryption, logging, or authentication. In design and architecture it appears as skipped secure-design principles such as least privilege and separation of concerns, shared accounts, weak permission models, and reliance on legacy or third-party components with known weaknesses. In implementation it appears as ignored input validation, hard-coded secrets, weak password rules, use of deprecated libraries, and minimal code review for security. In testing it appears when security testing is minimized, siloed, or deferred, with findings ticketed for later and then left unresolved. In deployment and operations it appears as over-permissive IAM policies, missing monitoring and logging, and patching delays. In maintenance it appears when insecure architectural assumptions remain embedded because fixes are risky, expensive, or likely to break production (Boufaied et al., 15 Jul 2025).

Illustrative scenarios are concrete. Practitioners reported login features shipped without MFA or strong password checks to meet MVP deadlines; shared admin accounts retained for convenience; over-broad AWS Lambda permissions granted “temporarily” for debugging and never tightened; missing or incorrectly implemented OTP features left unresolved because testing capacity was limited; and cases where poor key management or missing firewalls were known risks but not addressed early (Boufaied et al., 15 Jul 2025). These examples show that SD is produced both by initial shortcuts and by continuing neglect.

Architectural research on blockchain-based systems generalizes this formation logic. It argues that security debt is often rooted in sub-optimal design decisions involving blockchain type, storage and computation placement, consensus protocol, protocol configuration such as block size and confirmations, cryptographic primitives, key management, and smart contracts (Ahmadjee et al., 2019). This suggests that SD formation is not confined to code-level defects; it also emerges from early architecture and infrastructure decisions whose security consequences may only become visible later.

3. Risk semantics, consequences, and long-term accumulation

The immediate effect of Security Debt is increased attack surface and exploitability. Interviewed practitioners directly associated unresolved SD with vulnerabilities such as SQL injection, cross-site scripting, buffer overflows, insecure deserialization, and insecure direct object references, and reported real incidents including compromised systems, downtime, reputational damage, remotely controllable smart-grid devices, extractable passwords in desktop applications, and costly post-breach investigations (Boufaied et al., 15 Jul 2025). The literature therefore treats SD not as an abstract maintainability concern but as a concrete risk object.

The consequences are commonly framed through the CIA triad. For confidentiality, SD manifests as exposed passwords or tokens in URLs, robots.txt exposing sensitive directories, leaked wallet keys, leaked backend secrets, and privacy breaches. For integrity, it manifests as unauthorized data manipulation, altered banking details or transaction records, prompt injection affecting model behavior or training data, and tampering with application logic. For availability, it manifests as denial of service, crashes, resource exhaustion, and service outages enabled by postponed fixes (Boufaied et al., 15 Jul 2025). A single weakness such as SQL injection can simultaneously compromise all three dimensions.

Security-debt literature also stresses the temporal logic of accumulation. Almost all practitioners in the 2025 interview study regarded SD primarily as a long-term risk: unresolved flaws become entrenched, remediation costs rise as insecure assumptions spread through the codebase, and fragile security-sensitive components become harder to update or refactor (Boufaied et al., 15 Jul 2025). Blockchain-oriented work expresses the same temporal logic via the principal-and-interest metaphor. In that framing, principal is the work required to correct a sub-optimal design, while interest is the additional effort, damage, and exposure that accrue if the debt is left unpaid; interest may include security maintenance costs, trust rebuilding, reputation damage, and follow-up costs after leaks or misuse (Ahmadjee et al., 2019).

The accumulation of SD therefore has technical, organizational, and business dimensions. Technically, it increases maintenance overhead and may force system-wide refactoring or even full replacement. Organizationally, it creates recurring coordination problems between development, QA, product, and security functions. Commercially, it creates exposure to compliance violations, regulatory penalties, trust erosion, and post-incident crisis measures such as taking systems offline or outsourcing replacement (Boufaied et al., 15 Jul 2025). Taken together, these studies suggest that SD should be understood as a compounding liability whose “interest” is paid in both remediation cost and realized attack impact.

4. Assessment, metrics, and prioritization

In general software practice, SD identification and tracking rely on a mixture of manual and automated mechanisms. Reported manual practices include code reviews focused on common weaknesses, manual log review, exploratory security testing, and backlog tracking in Jira, HubSpot, or Git issue trackers. Automated practices include static and dynamic analysis, cloud monitoring and logging such as AWS CloudWatch and CloudTrail, vulnerability scanners, internal security dashboards, and CI/CD security checks, although integration into everyday delivery pipelines remains inconsistent (Boufaied et al., 15 Jul 2025). Practitioners typically prioritize SD by severity or exploitability, impact on CIA and business, complexity or effort to fix, and client pressure or contractual obligations (Boufaied et al., 15 Jul 2025).

The same practitioner study notes that industry still lacks consistent, interpretable security metrics. In practice, teams often rely on counts of vulnerabilities grouped by severity, time-to-fix, and regulatory compliance status, while some research uses technical-debt indicators such as code smells, bugs, and duplication as proxies for vulnerabilities (Boufaied et al., 15 Jul 2025). This suggests an informal prioritization function over severity, impact, likelihood, effort, and client importance rather than a universally standardized metric.

A more explicit quantitative framework appears in smart-contract research. “Assessing Smart Contracts Security Technical Debts” proposes a two-step debt-aware assessment approach: first identify design vulnerabilities through automated tools and manual analysis; then estimate their ramifications using principal and interest (Ahmadjee et al., 2021). In this model, principal is the gas cost of remediation:

$P_{Security\ Debt} = Gas\_D(c) + Gas\_U(c)$

where $Gas_D(c)$ is the gas needed to deploy the repaired contract and $Gas_U(c)$ is the gas needed for the update pattern that transitions users to the repaired contract (Ahmadjee et al., 2021). Interest is modeled as accumulated risk over the contract’s lifespan:

$$AI_{Security\ Debt} = CWSS\ score \times CAL \times CLS$$

where $CWSS\ score$ captures technical and business severity, $CAL$ is Contract Activity Level, and $CLS$ is Contract Lifespan (Ahmadjee et al., 2021).

That same work operationalizes prioritization through a cost/value severity matrix. For the reported experiment, cost thresholds were low up to $R$0, and high above that; interest thresholds were low up to 100, medium up to 200, and high above 200; high cost plus high value was categorized as Critical (Ahmadjee et al., 2021). This is domain-specific, but it shows how SD can be made actionable by combining remediation cost with exposure over time rather than tracking only defect counts.

5. Domain-specific manifestations in blockchain and smart contracts

Blockchain research strongly rejects the misconception that blockchain-based systems are secure by design. The taxonomy introduced in “A Taxonomy for Understanding the Security Technical Debts in Blockchain Based Systems” argues that decentralized systems inherit both traditional security debt and blockchain-specific debt arising from ill-informed architectural choices (Ahmadjee et al., 2019). The principal design spaces singled out as debt-prone are blockchain type, storage and computation, consensus protocol, protocol configuration, cryptographic components, key management, and smart contracts (Ahmadjee et al., 2019).

These architectural decisions create debt by misaligning trust assumptions, privacy requirements, integrity guarantees, and availability needs. Public chains may incur debt if transparency is used without additional confidentiality controls, creating privacy breaches or linkability. Private and consortium chains may incur debt when a small number of nodes or centralized governance introduce DDoS exposure, compromised trust assumptions, or coordinated-failure risks. On-chain versus off-chain storage decisions may create debt through permanent public exposure of sensitive data or, conversely, by reintroducing centralized off-chain single points of failure. Consensus and protocol configuration choices may create debt by making 51% attacks, selfish mining, double spending, or fork instability easier. Weak or aging hash functions, fragile signature schemes, omitted encryption, and single-key management create debt in cryptographic and identity layers (Ahmadjee et al., 2019).

Smart contracts add a distinctive form of design-time Security Debt because deployed code is immutable. The smart-contract assessment literature therefore focuses on design vulnerabilities such as Front-Running, Time Manipulation, Denial of Service, Arithmetic Issues, Bad Randomness, Sensitive Data Exposure, Using Components with Known Vulnerabilities, Improper Inheritance, and Modularity Violation, treating these categories as indicators of technical debts (Ahmadjee et al., 2021). Because remediation requires redeployment and update patterns rather than simple patching, the debt metaphor becomes unusually concrete.

Case studies make the domain-specificity visible. The blockchain taxonomy uses uPort and a Hyperledger-based EMR system to illustrate debt arising from public-chain transparency without confidentiality measures, single private keys stored on mobile devices, insecure default MAC/KEX/cipher settings, centralized off-chain storage, MD5 usage, and single key pairs for signing and encryption (Ahmadjee et al., 2019). The smart-contract study’s CEOThrone example treats variable shadowing as Improper Inheritance; it reports a deployment cost of 897,200 gas, an estimated remediation cost of approximately $R$1, and an estimated accumulated interest of approximately $R$2, placing the debt item in the high-interest category (Ahmadjee et al., 2021). These studies suggest that SD is not only a general software-maintenance metaphor but also a practical design-analysis instrument in systems where patchability, transparency, and monetary exposure are unusually constrained.

6. Self-admitted security debt, public traces, and tool complementarity

A substantial research strand treats self-admitted technical debt as an observable surface of Security Debt. In Chromium C code, “WeakSATD: Detecting Weak Self-admitted Technical Debt” identified 847 SATD blocks across 41,753 C files; 465 of those SATD blocks contained at least one weak-code snippet derived from CWE heuristics, meaning 55% of SATD blocks were weak SATD, spanning 14 distinct CWE issues (Russo et al., 2022). The approach was deliberately lightweight: mine CWE examples, derive regex heuristics, detect SATD comments using 62 patterns, associate comments with code blocks, and then identify SATD blocks that also match weak-code patterns. The result was a concrete mechanism for treating a subset of SATD as security-relevant technical debt (Russo et al., 2022).

A broader mixed-methods study over 103 Apache OSS projects analyzed 8,812 SATD instances and identified 201 security-related SATD instances, or 2.28% of all SATD, mapped to 25 different CWE types, 8 of which appeared in MITRE’s 2023 Top-25 most dangerous software weaknesses (Ferreyra et al., 2024). Its accompanying survey of 222 OSS practitioners found that security pointers are used to improve project quality, facilitate collaboration and documentation, promote a security culture, support compliance, and serve as self-reminders, but are simultaneously viewed as risky because they may expose vulnerabilities, create security misconceptions, or leak sensitive information (Ferreyra et al., 2024). The study therefore frames SSATD as both a signal of known security debt and a potential disclosure hazard, emphasizing the need to preserve the contextual integrity of security pointers.

Public developer discourse shows the same phenomenon at larger scale. “Exploring Technical Debt in Security Questions on Stack Overflow” mined 117,233 security-related questions and classified 45,078 of them as security-related technical debt questions, corresponding to 38% of the security-question corpus (Edbert et al., 2023). The most debt-prone tags were security, websecurity, sql-injection, xss, and csrf, and the debt-related questions were longer and more implementation-oriented than non-debt security questions; askers also tended to have higher reputation and slightly older accounts (Edbert et al., 2023). This suggests that developers frequently discuss suboptimal security practices without explicitly using the term “technical debt,” especially when dealing with prevention mechanisms and implementation trade-offs.

Recent work also investigates whether SSATD complements static analysis tools. “Reading Between the Code Lines: On the Use of Self-Admitted Technical Debt for Security Analysis” constructed a dataset of 135 validated security-related SATD instances from the MADE-WIC Big-Vul partition; combined static analysis tools flagged 114 of them, spanning 24 distinct CWE identifiers, while manual SSATD mapping yielded 33 unique CWE types (Ferreyra et al., 3 Feb 2026). Only 6.42% of the manual-tool CWE pairs were judged closely related, and 21 SSATD cases were missed entirely by all tools, including cases mapped to race conditions, improper initialization, resource leaks, and improperly implemented security checks (Ferreyra et al., 3 Feb 2026). A survey of 72 security-aware developers reported that SSATD is frequently used to complement SAT outputs, especially for understanding root causes, impact, affected components, and suitable fixes; for fixing race conditions, SSATD was rated significantly more helpful than SATs (Ferreyra et al., 3 Feb 2026). Taken together, these studies suggest that developer-authored artefacts are not merely qualitative annotations but an important empirical substrate for locating and classifying SD.

7. Communication, governance, misconceptions, and research frontiers

In organizational practice, Security Debt is communicated through backlog tools and ticketing systems, meetings and informal conversations, and reports or dashboards that expose open vulnerabilities, trends, and severity ratings (Boufaied et al., 15 Jul 2025). Effective communication strategies frame SD in business and risk terms, relate each item to CIA dimensions, use structured risk ratings such as critical/high/medium/low, and make the security backlog visible alongside feature work (Boufaied et al., 15 Jul 2025). The recommendation is not merely procedural: when SD is discussed only as a technical detail, it competes poorly with roadmap commitments and revenue-facing functionality.

Several misconceptions recur in the literature. One is that SD is just ordinary technical debt; empirical work instead shows that practitioners distinguish SD by its exploitability and direct CIA impact (Boufaied et al., 15 Jul 2025). Another is that tool scarcity is the main bottleneck; the 2025 practitioner study explicitly argues that time pressure and functional prioritization are more dominant causes than lack of tools (Boufaied et al., 15 Jul 2025). A third is that blockchain systems are secure by default; blockchain-specific work rejects this and treats many architectural choices as direct sources of security technical debt (Ahmadjee et al., 2019). A fourth is that documenting security debt is unambiguously beneficial; SATD studies show a genuine controversy, because public security pointers can improve collaboration and awareness while also facilitating vulnerability exploits or zero-day exposure if contextual integrity is broken (Ferreyra et al., 2024).

Governance mechanisms proposed across the literature are consistent. Teams are urged to treat SD as strategic risk, integrate it into planning and backlog management, prioritize it using severity and impact, invest in training and security champions, standardize tool usage, and ensure clear ownership for remediation (Boufaied et al., 15 Jul 2025). Shift-left integration across the SDLC—security requirements, threat modeling, secure design principles, dependency management, security testing in CI/CD, configuration hardening, and continuous monitoring—is repeatedly presented as the preferred way to prevent debt formation rather than merely service accumulated debt (Boufaied et al., 15 Jul 2025).

Research gaps remain substantial. Practitioner work calls for better metrics and models linking SD to business risk, more longitudinal and large-scale studies, and closer analysis of decision-making and trade-offs rather than tool-centric explanations (Boufaied et al., 15 Jul 2025). Blockchain taxonomy work remains largely conceptual and qualitative (Ahmadjee et al., 2019). WeakSATD relies on regex heuristics over a single C project and is explicitly presented as a preliminary study (Russo et al., 2022). SSATD mining across Apache projects depends on keyword filtering and subjective CWE mapping, and Stack Overflow evidence is confined to public discourse rather than internal enterprise settings (Ferreyra et al., 2024); (Edbert et al., 2023). Tool-complementarity studies currently cover a limited set of languages, datasets, and default tool configurations (Ferreyra et al., 3 Feb 2026). A plausible implication is that a mature SD discipline will need to combine practitioner-centered governance, domain-specific assessment models, artifact mining, and standard weakness taxonomies such as CWE, rather than relying on any single measurement or detection paradigm.