Post by Amr
Think of your tech stack as an unpaid credit card bill that you’ve been racking up charges on for years. You swipe for those quick wins, like rushing a feature to market or patching a system with a temporary fix, to keep things moving in the moment. It feels harmless at first, maybe even smart. But over time, the interest accrues relentlessly. What started as a small balance balloons into something unmanageable, with late fees, penalties, and the constant threat of collection calls. In the world of software and infrastructure, this balance is known as technical debt: the implied future cost of reworking or fixing the shortcuts, compromises, and suboptimal decisions made during development to prioritize speed, cost savings, or immediate functionality over long-term quality and maintainability.
Technical debt, a term coined by Ward Cunningham in the early 1990s, isn‘t inherently evil—it’s often a deliberate tradeoff in agile environments where getting to market fast can mean survival. But like financial debt, it compounds if ignored, leading to increased complexity, reduced velocity, and heightened risks. In simple terms, technical debt is the shortcuts taken in development that save time now but create problems later. These shortcuts can stem from various sources: tight deadlines forcing incomplete testing, evolving business needs outpacing original designs, or even team turnover leaving knowledge gaps. The result? A codebase or infrastructure that’s harder to maintain, scale, and secure.
To break it down further, technical debt manifests in several distinct types, each with its own flavor of pain points:
In the cybersecurity industry, any of these debt types amplify risks exponentially. For instance, poorly configured cloud environments, such as AWS S3 buckets left public or Azure VMs with default credentials, create immediate exposure points for data exfiltration. Rushed deployments of LLMs introduce unique debts, like unvalidated training data leading to model biases or weak API endpoints vulnerable to prompt injection attacks, where adversaries craft inputs to manipulate outputs or extract sensitive information. Add to that legacy codebases with hardcoded secrets (e.g., API keys embedded in source code instead of using secure vaults like HashiCorp Vault), outdated dependencies harboring known vulnerabilities (think Log4j’s infamous CVE-2021-44228), and insufficient access controls in APIs that allow privilege escalation through techniques like OAuth misconfigurations.
These aren’t abstract issues; they‘re real-world accumulators that hackers exploit daily.
Why should we care, especially in a cybersecurity context? Because technical debt isn’t just a productivity killer for developers—it’s a critical cybersecurity risk that organizations often overlook until it’s too late. In fast-paced digital transformations, debt contributes to an expanded attack surface, where small oversights evolve into exploitable weaknesses. Hackers don’t need to be geniuses; they just need to find the low-hanging fruit your team deferred fixing. With breaches costing millions and regulations tightening, ignoring the accumulation of technical debt is like betting your house on a leaky roof during a storm. Understanding and addressing it isn’t optional—it’s essential for resilience in an era where threats evolve as fast as your tech.
Technical debt doesn’t arrive with fanfare, drawing your awareness; it accumulates stealthily, eroding your security posture like rust on untreated metal. What begins as a minor compromise for expediency morphs into systemic weaknesses that threat actors—ranging from opportunistic script kiddies to sophisticated nation-state groups—eagerly exploit. As a hacker, I see the consequences of technical debt all the time. Let’s go through the main ways debt introduces vulnerabilities.
Let’s begin with the most commonly seen debt: unpatched and outdated systems, a hallmark of infrastructure debt. Legacy environments, such as servers running unsupported versions of Windows (Microsoft ended support for Windows 10 on October 14, 2025), or deprecated libraries like old OpenSSL or Log4j builds are prime targets because vendors stop issuing security fixes. Neglecting updates means leaving known CVEs—vulnerabilities cataloged in databases such as NIST’s NVD—unaddressed. For instance, a high-severity remote code execution (RCE) flaw such as CVE-2025-49844 (a Redis use-after-free vulnerability that allows authenticated attackers to escalate a crafted Lua script into host code execution) has already been exploited in the wild, illustrating how a single unpatched component can give adversaries a foothold. Historic Java ecosystem failures like Log4Shell (CVE-2021-44228) show how a vulnerable library can cascade through an enterprise stack and enable unauthenticated RCEs that lead to data exfiltration and ransomware. Scanning tools such as Shodan or Masscan make identifying exposed legacy services trivial by fingerprinting outdated banners, and when a significant portion of an environment runs unsupported OS or end-of-life middleware, ransomware operators frequently exploit that debt to encrypt data or steal records. The danger isn’t only zero-day exploits—patched “n-day” vulnerabilities remain available to attackers when remediation is delayed, producing exposure windows measured in weeks or months.
Next, rushed and sloppy coding practices fuel insecure applications with exploitable flaws. In the sprint to ship, developers might forgo proper input validation, paving the way for injection attacks. Consider SQL injection. Without parameterized queries, user-supplied data like “‘; DROP TABLE users; –” can execute arbitrary database commands, dumping or altering records. Hardcoded credentials exacerbate this, and private information like database passwords baked into code is easily extracted via decompilers like Ghidra. For LLMs rushed into production, debt manifests in insecure fine-tuning processes; without robust guardrails, attackers can use adversarial examples to poison models, causing them to leak training data or generate malicious content. Recent analyses show that 40% of open-source ML repos contain such flaws, often from copied code snippets without security reviews. Overly permissive error handling, like verbose stack traces exposing internal paths, further aids reconnaissance.
Misconfigured and exposed setups represent another debt vector, particularly in cloud-native environments. Configurations drift over time; initial setups for speed overlook best practices, like setting AWS IAM policies too broadly or neglecting Kubernetes network policies. This leads to exposures such as public-facing Elasticsearch clusters without authentication, allowing unauthenticated queries to harvest data. Technically, server-side request forgery (SSRF) thrives here: An attacker tricks a vulnerable app into making internal requests (e.g., fetching metadata from http://169.254.169.254 in AWS to steal temporary credentials and exploit chain escalations). In IoT integrations, misconfigs like default MQTT broker ports open to the internet enable man-in-the-middle attacks, intercepting device communications. Reports indicate that misconfigs accounted for 30% of cloud breaches in 2025, often rooted in debt from unrefactored legacy migrations.
Lastly, undetected and ignored issues arise from inadequate security testing and vulnerability management. Debt in this area means skipping static application security testing (SAST) or dynamic analysis (DAST) in pipelines, letting flaws like cross-site scripting (XSS) or buffer overflows slip through. XSS injects malicious scripts into web pages, stealing session cookies via <script>alert(document.cookie)</script>. Without regular scans or dependency checks (e.g., using OWASP Dependency-Check), outdated libraries accumulate CVEs. Shadow IT compounds this; unmonitored SaaS apps or employee devices create blind spots for lateral movement, where attackers pivot using stolen credentials. In essence, this debt fosters an ecosystem of hidden risks, where the lack of visibility turns minor issues into full-blown compromises, emphasizing the need for proactive debt audits.
Any of these sound familiar to your team?
Theory is one thing, but real-world breaches paint a vivid picture of how technical debt turns into ticking time bombs. Hackers will hack; they’re relentless in probing for weaknesses, and debt provides the perfect entry points. Below, we examine 2025’s notable incidents, focusing on how unpatched systems, misconfigs, rushed deployments, and ignored testing contributed directly to these cases’ notoriety, with technical breakdowns of the vulnerabilities involved.
One standout is the McDonald’s AI-driven hiring platform breach in July 2025, affecting millions of job applicants. Rooted in rushed LLM integration without proper governance, attackers exploited prompt injection vulnerabilities in the AI screening tool. By crafting resumes with adversarial text, they bypassed filters to extract applicant data, including SSNs and contact info. This debt from hasty deployment without proper attention to access controls highlighted shadow AI risks, costing McDonald’s an estimated $150 million in remediation and fines.
The United Natural Foods ransomware attack in June 2025 stemmed from legacy systems debt. Unpatched VPN appliances (CVE-2024-28995 in FortiGate) allowed initial access via credential stuffing, leading to lateral movement and the encryption of supply chain data. Technically, the flaw enabled remote unauthenticated command execution, disrupting food distribution nationwide. The debt at play here? Delayed updates on end-of-life hardware, contributing to costs of $200 million.
TransUnion’s August 2025 incident involved misconfigured cloud storage, exposing credit data for 200 million individuals. A public S3 bucket that was left open due to config drift from unrefactored migrations permitted SSRF attacks, where bots fetched internal files. This classic debt outcome led to identity theft spikes and $300 million in settlements.
The Chinese Surveillance Network mega-breach in June 2025 leaked 4 billion records from outdated IoT cameras. Unpatched firmware (multiple CVEs in Hikvision devices) allowed botnet recruitment via default credentials, turning cameras into data exfiltration nodes. Debt from ignored updates in vast deployments underscored global supply chain risks.
Air France’s data leak in August 2025 was tied to rushed API coding, with insufficient authorization checks enabling OAuth token hijacking. Attackers impersonated users to access passenger details, a flaw from code debt in legacy integrations.
This is only a handful of cases from 2025, and they illustrate very prominent patterns: Debt delays patches, ignores configs, and skips tests, enabling exploits like RCE, injection, and misconfig abuses. In 2025, ransomware surged 24%, often via debt-fueled access, proving the direct link to disasters.
Let’s look at a brighter side. Measuring technical debt transforms it from a nebulous concept into a manageable metric, enabling teams to prioritize and track progress. In cybersecurity, this quantification reveals risk exposure, guiding resource allocation. Let’s explore key metrics, tools, and best practices tailored for 2026’s landscape.
Core metrics:
Google’s approach combines quarterly dev surveys (rating friction on a 1–5 scale), with log analysis for empirical data, like dependency age or refactor frequency.
Advanced KPIs include the Security Debt Index, blending CVSS scores with business impact, or the Change Failure Rate (CFR) from DORA metrics. A high CFR (> 30%) flags debt-impacted deployments. Testing coverage metrics (e.g., > 80% unit tests) highlight ignored areas.
Tools automate survey results. SonarQube scans for code smells, security hotspots, and debt estimates in hours. CAST Highlight assesses portfolio debt across languages, scoring maintainability. CodeScene visualizes hotspots via behavioral code analysis. vFunction specializes in architectural debt, identifying microservices candidates. Zenhub integrates with Jira for tracking debt as backlog items. For cyber-specific cases, Nessus or Qualys integrate vulnerability scoring; the Bugcrowd Platform uses crowd intel for debt mapping.
When it comes to best practices, the baseline is carrying out scans, setting thresholds (e.g., debt < 10% of sprint), and conducting quarterly reviews. It’s important to tie metrics to OKRs for accountability. AI-assisted tools like Emma’s tracker or PropelCode’s dashboards automated insights, reducing manual effort.
The repercussions of cybersecurity technical debt extend far beyond code, impacting finances, reputations, and operations with force. IBM’s 2025 Cost of a Data Breach Report revealed a global average of $4.44 million per breach, down 9% from 2024, but U.S. incidents have spiked to $10.22 million, with healthcare costing $10.93 million. Malicious insiders, often leveraging debt, contribute to $4.92 million in costs on average.
Financially, breaches dominate. Direct costs include forensics ($1–2M), notifications ($500K+), and lost business (up to 40% churn). Ransomware, up 24% in 2025, demands multimillion payouts, with recovery averaging 23 days. Operational fallout is also significant; downtime doubles costs, with centralized systems amplifying risks (e.g., a single unpatched server halting production).
Reputationally, trust erodes fast; post-breach stock drops average 7%, with customer loss persisting years, as in Equifax’s lingering fallout. Social media amplifies damage, with X threads dissecting failures.
Furthermore, remediation expenses overshadow any security costs. Fixing something post-breach is 3–5x pricier than prevention, involving audits, rebuilds, and PR. Then there are the legal woes and resulting fines; GDPR mandates up to 4% of revenue and HIPAA $50K per violation, plus other legal battles, as seen in 2025’s class actions.
Overall, debt’s toll can be 42% of dev time wasted and the potential of $460M in losses from a single incident.
As 2025 unfolds, technical debt in AI/ML and IoT ecosystems emerges as a frontier threat, amplifying traditional risks with new vectors. AI hype has waned, but ungoverned deployments create debt. Lacking controls like access policies, shadow AI has spiked breaches by 200%. Furthermore, attackers use AI for adaptive phishing or deepfakes, bypassing defenses. Notably, 97% of AI incidents stem from poor governance. Model poisoning via tainted data or supply chain attacks (e.g., huggingface repos) leads to biased outputs or backdoors.
Additionally, IoT threats have exploded; there are 820,000 daily attacks on devices, with botnets like Mirai variants recruiting via default credentials. Debt in firmware updates leaves billions vulnerable, and industrial IoT debt risks shutdowns, as observed in 2025’s OT ransomware surges. Quantum threats loom, cracking outdated encryption; supply chains face zero days sold on dark markets.
Mitigation demands debt audits in these areas, as AI/IoT debt could double breach costs by 2026.
Repaying technical debt demands a structured approach, blending process, tools, and culture. Here are five places to start:
Technical debt is a stealthy, dangerous cybersecurity threat, breeding vulnerabilities that hackers exploit for breaches, ransomware, and chaos. From unpatched legacies to AI governance gaps, it accumulates silently but detonates with multimillion-dollar impacts on finances, operations, and trust. The message is clear: pay now through audits and fixes, or pay much, much more later when the inevitable crises have surfaced.
Organizations, hackers, and researchers—audit your debt, prioritize with tools like Bugcrowd, and build resilient cultures. Start today: scan your stack, integrate security, and turn debt into strength. Your security depends on it.
With Bugcrowd’s newest acquisition of Mayhem Security, organizations can minimize tech debt and build resilient security cultures. Check out this blog to learn more.