Why Embedded Software Teams Need Better Build Pipelines

Embedded software development has long trailed the broader software industry when it comes to adopting modern engineering practices. In the late 1990s, the push was simply to move away from assembly language and toward structured C development. Today, while the general software industry has standardized on build pipelines, continuous integration, automated testing, and CI/CD workflows, many embedded teams — particularly at smaller companies — are still relying on largely manual, ad-hoc processes.

The gap matters. A well-designed build pipeline does not just speed up software delivery — it improves quality, reduces debugging time, lowers project costs, and makes firmware deployments more predictable and repeatable. This article explains what a modern embedded software build pipeline looks like, and how teams can incrementally implement one.

What Is a Software Build Pipeline?

A software build pipeline is the collection of processes and tools used to manage, build, test, and deploy an application in a structured, repeatable way. In a traditional embedded workflow, a developer writes code, manually tests it, and produces a firmware image that is manually flashed to the target device. This approach works at small scale, but it does not scale gracefully as team size or codebase complexity grows.

A modern build pipeline replaces manual, error-prone steps with automation. The core benefits include:

• Improved software quality through consistent, automated checks at every commit
• Reduced debugging time by catching defects early, before they compound
• Lower project costs by reducing rework and late-stage bug discovery
• Better deadline predictability through continuous visibility into code health
• Simplified deployment via automated, gated firmware delivery

The Four Stages of a Modern Embedded Build Pipeline

A practical embedded CI/CD pipeline can be broken down into four sequential stages. The first stage is triggered manually by the developer; the remaining three can be fully automated.

Stage 1: Commit (Manual Trigger)

The developer commits source code to a version control repository. This commit is the event that triggers all downstream automation. The quality of the pipeline depends heavily on how frequently developers commit. Small, frequent commits — ideally at least once per day — provide faster feedback loops and make it far easier to isolate the cause of a build or test failure.

Stage 2: Build and Analysis (Automated)

Once a commit lands, the CI server picks it up and triggers a full build of the firmware. This stage should also run static code analysis tools that examine the source for common defects, coding standard violations, and security vulnerabilities without executing the code. Both the compiler and static analysis tools should be invokable from the command line or expose plugins compatible with your CI server so that these steps require no human intervention.

Stage 3: Test and Reporting (Automated)

After a successful build, the pipeline runs automated tests and generates reports. These tests may run on a host-based simulator, an emulator, or actual target hardware depending on the maturity of the test infrastructure. Results — including pass/fail status, code coverage metrics, and any warnings from the analysis stage — are reported back to the developer. If anything fails at this stage, the pipeline stops and no deployment occurs. The developer is notified and the cycle restarts with a new commit after fixes are applied.

Stage 4: Deployment (Automated or Manual)

Only firmware that has passed all build, analysis, and test gates proceeds to deployment. Depending on the product and risk tolerance, deployment can be fully automated — pushing firmware to devices in the field via OTA (over-the-air) update mechanisms — or it can require a manual approval step before release. Either approach is valid; what matters is that the gate between test and deployment is enforced by the pipeline, not by human memory.

Building the Pipeline Incrementally

Setting up a full embedded CI/CD pipeline is not something that happens overnight. It requires research, tooling decisions, implementation work, and team training. The practical approach is to add each stage incrementally, making each one solid before moving on to the next.

Step 1: Establish Robust Version Control Practices

Most embedded teams now use some form of version control, which is a meaningful improvement from even a few years ago. However, committing code once a week — or less — is still common and largely defeats the purpose. The pipeline can only provide timely feedback if commits are frequent. Aim for at least one commit per developer per day, ideally more. This discipline also forces developers to break work into smaller, reviewable increments, which itself improves code quality.

Step 2: Stand Up a Continuous Integration Server

A CI server is the orchestration engine of the pipeline. It monitors the repository for new commits, triggers builds, runs analysis and tests, and reports results. Jenkins is one of the most widely adopted CI servers and has a large ecosystem of plugins relevant to embedded development, including support for various compilers and test frameworks. Other options exist and the right choice depends on your existing toolchain and infrastructure. What matters is that the CI server can be configured to execute your build and test steps automatically without manual intervention.

Step 3: Automate the Compiler and Add Static Analysis

Your compiler must be executable from the command line so the CI server can invoke it without a GUI. Most professional embedded compilers support this, but verify your toolchain does before committing to a CI approach. If your team does not yet use a static code analysis tool, this is an appropriate point to evaluate and adopt one. Static analysis catches entire classes of defects — null pointer dereferences, buffer overflows, uninitialized variables, MISRA violations — that compiler warnings alone will not surface. Integrating static analysis into the automated pipeline means every commit is checked, not just when a developer remembers to run the tool manually.

Step 4: Implement a Test Harness

Automated testing is the most valuable and also the most challenging stage to add to an embedded pipeline. Unlike application software, embedded code often has tight hardware dependencies that complicate host-based unit testing. Hardware Abstraction Layers (HALs) and dependency injection patterns can help decouple business logic from hardware-specific code, making more of the codebase testable without physical hardware.

Test harnesses for embedded C/C++ such as Unity, CppUTest, or GoogleTest can run unit tests on the host machine as part of the CI pipeline. For integration tests that require hardware, Hardware-in-the-Loop (HIL) test rigs can automate execution against real targets. Regardless of approach, the most important principle is to build tests alongside the code — not after the fact. Test-Driven Development (TDD) is one effective methodology for achieving this. Automated tests that cover only a fraction of the codebase can create false confidence; invest in meaningful coverage from the beginning.

Key Tools to Consider

Note: Tool selection should be driven by your target architecture, compiler ecosystem, and team familiarity. The table above lists representative options, not exhaustive recommendations.

Conclusion

Modernizing the embedded software build pipeline is a meaningful investment, but one that pays dividends throughout the entire product lifecycle. Faster feedback, earlier defect detection, and automated deployments reduce the unpredictability that plagues so many embedded development projects. The key is not to attempt the full pipeline transformation in a single sprint. Start with solid version control practices, add a CI server, automate the build and analysis stages, then gradually introduce automated testing. Each stage you add compounds the benefits of the previous one. Over time, you end up with a pipeline that gives your team continuous visibility into software health and the confidence to deploy firmware reliably.

Overview

An India-based security engineer named Avinash Jain discovered that hundreds of organizations — including NASA, Google, Yahoo, Western Union, Lenovo, and numerous government agencies — had misconfigured their Jira project management servers in ways that exposed internal project data, employee names, and email addresses to the open internet. The exposed data was indexed by search engines, making it discoverable by anyone with knowledge of advanced search operators (commonly called Google Dorks).

The vulnerability was not a software bug in Jira itself, but rather the result of default permission settings that many administrators misunderstood. The research was reported by Bleeping Computer and drew widespread attention from the security community.

What Is Jira?

Jira is a proprietary issue tracking and project management platform developed by Atlassian. It is widely used for bug tracking, agile sprint planning, and task management across software development teams. Its popularity among Fortune 500 companies and government agencies makes misconfigurations at scale particularly consequential.

Root Cause: Default Permission Settings

The core problem stemmed from how Jira handles the default visibility of filters and dashboards. When a user creates a new filter or dashboard in Jira, the default visibility is set to “All users” (for filters) and “Everyone” (for dashboards). Many administrators and end users interpreted these labels as meaning “everyone within the organization.” In practice, these settings expose the content to any user on the public internet, including unauthenticated visitors.

Jira’s own documentation includes a disclaimer for the “Public” sharing option: “If a filter or dashboard is shared with the Public, the name of the filter or dashboard will be visible to anonymous users.” However, many administrators either missed this warning or did not appreciate its implications for internet-facing Jira Cloud instances.

An additional setting in the Global Permissions menu allows administrators to grant access to the “Anyone” user group, which includes users who are not logged in. Atlassian’s own documentation explicitly notes this is not recommended for systems accessible from the public internet, including Jira Cloud.

What Data Was Exposed?

Because Jira includes a user picker functionality that enumerates account users, misconfigured servers exposed the following types of sensitive information to anonymous visitors:

• Full names and email addresses of employees across affected organizations
• Employee roles, as inferred from Jira group memberships
• Active and upcoming projects, milestones, and task assignments via dashboards and filters
• Internal project structure and workflow details

Jain noted in his report: “Thousands of companies’ filters, dashboards and staff data were publicly exposed. It occurs because of the wrong permissions scheme set to filters and dashboards, hence providing their access even to non-logged-in users and leading to leaking of sensitive data.”

Affected Organizations

According to Jain’s research, affected organizations spanned private industry and the public sector globally. He cited examples including:

• Technology companies: Google, Yahoo, HipChat, Zendesk, Dubsmash
• Enterprise and financial firms: Western Union, Lenovo, Informatica, Sapient, 1Password
• Ride-sharing and consumer apps: Go-Jek
• Government and public sector: NASA, the United Nations, the Brazilian government transport portal, the Canadian government finance portal, and an unspecified European government portal

Bleeping Computer independently verified the vulnerability and confirmed they were able to locate government domains, private firms, and educational institutions through standard search engine queries.

How the Servers Were Discovered

Jain identified the vulnerable servers using Google Dorks — specialized search operators that target publicly indexed pages with specific URL patterns or content characteristics typical of Jira dashboards and user picker endpoints. Because the exposed pages were indexed by major search engines, no authentication or exploitation tool was required to locate them.

This highlights a significant operational security risk: data exposed through misconfiguration is often passively discoverable long before any targeted attacker finds it. In this case, the same technique could be used by competitors conducting corporate espionage or by threat actors mapping out personnel and project structures for phishing or social engineering campaigns.

Disclosure and Prior Research

Jain responsibly disclosed his findings to the affected organizations. This was not his first discovery of this type: in a prior engagement, he had identified a misconfigured Jira server exposing names and email addresses of approximately 1,000 NASA users. That issue was reported to NASA and subsequently fixed.

How to Audit and Fix Jira Permissions

Organizations running Jira Cloud or Jira Server instances should audit their permission configurations immediately. Key areas to review:

• Global Permissions: Navigate to Jira Administration → System → Global Permissions and confirm that the “Anyone” group is not granted “Browse Users,” “Create Shared Objects,” or similar permissions on internet-facing instances.
• Filter and Dashboard sharing: Audit existing filters and dashboards for those shared with “Public” or “Everyone.” Change these to specific groups or project roles where broad sharing is not required.
• Anonymous access: Disable anonymous access for any Jira project that does not have a deliberate and documented business reason to be publicly readable.
• User picker exposure: Ensure that the user picker REST endpoint (/rest/api/2/user/picker) is not accessible to unauthenticated users by reviewing Global Permissions for the “Browse Users” permission.

Atlassian provides documentation on managing global permissions and project permissions in both Jira Cloud and Jira Server/Data Center editions. Organizations should treat a periodic permissions audit as a standard part of their DevOps security posture, not a one-time configuration task.

Broader Lessons for DevOps Teams

This incident is a textbook example of a misconfiguration vulnerability — a category that consistently ranks among the most common causes of data exposure in cloud and SaaS environments. Several takeaways apply broadly:

• Default settings are not safe defaults. Convenience-oriented defaults in SaaS tools often prioritize ease of collaboration over least-privilege access. Always review defaults before deploying to a production or internet-accessible environment.
• Labels can mislead. Terms like “Everyone” or “All users” are ambiguous in a SaaS context. Read the accompanying documentation to understand whether the scope is organizational or public internet.
• Search engine indexing amplifies exposure. Data that is publicly accessible will eventually be indexed. Assume that any misconfigured public endpoint will be discoverable without any active scanning by an attacker.
• Routine audits matter. Permission drift occurs as teams grow and configurations change. Automated audits of permission settings should be integrated into security review cycles.

The Jira misconfiguration case serves as a reminder that securing DevOps tooling is as important as securing the applications those tools help build.

Overview

Misconfigured Jira instances operated by well-known technology companies, government bodies, and educational institutions were found to be exposing sensitive internal data to unauthenticated users on the public internet. The exposure included employee names, email addresses, job roles, and details about ongoing projects — information that could be exploited for reconnaissance, targeted phishing, or competitive intelligence gathering.

The issue was discovered and disclosed by Avinash Jain, a security engineer, who reported findings to affected organisations including NASA and the United Nations. BleepingComputer independently verified that the misconfigured instances could be located using advanced search operators (commonly known as Google Dorks).

What Is Jira and Why Does It Hold Sensitive Data?

Jira, developed by Atlassian, is one of the most widely adopted project management and issue-tracking platforms in the world. Agile engineering teams use it to track tasks, bugs, sprints, and releases. Because Jira sits at the heart of development workflows, it inevitably contains details about internal projects, team structures, personnel, and organisational priorities — making it a high-value target if access controls are misconfigured.

The Root Cause: A Visibility Misconfiguration

According to Jain, the core problem lies in how Jira Cloud handles the default visibility setting for filters and dashboards. When a user creates a new filter or dashboard, the default sharing option is set to “All”. Many administrators and users reasonably interpret this to mean “all members of the organisation,” but in practice it means all users on the internet — including unauthenticated visitors.

Two configuration paths contribute to this exposure:

• Public filter and dashboard sharing: Jira Cloud allows filters and dashboards to be shared with a “Public” option. Atlassian’s own documentation warns that “if a filter or dashboard is shared with Public, the name of the filter or dashboard will be visible to anonymous users.” Many organisations enabled this without realising the full scope of what becomes accessible.
• Global Permissions — “Anyone” access: In the Global Permissions menu, administrators can grant access to users who are not logged in by selecting “Anyone” from a drop-down list. Atlassian’s documentation explicitly notes this is not recommended for systems accessible from the public internet, such as Jira Cloud. Despite this warning, many organisations left this setting enabled.

Once anonymous access is enabled at either level, Jira’s built-in user picker functionality can be used to retrieve a complete list of usernames and email addresses registered on that instance — without any authentication required.

How Misconfigured Instances Were Discovered

Jain used Google Dorks — carefully crafted search queries that leverage advanced Google search operators — to identify publicly accessible Jira instances exposing user and project data. This technique requires no special tools or credentials; it relies entirely on data that search engines have already indexed from misconfigured public-facing servers.

BleepingComputer confirmed that applying these search operators surfaced government domains, private companies, and educational institutions among the affected instances. The breadth of exposure was significant: Jain reported finding misconfigured Jira accounts across hundreds of companies, including organisations listed in the Alexa and Fortune top rankings.

“Thousands of companies’ filters, dashboards and staff data were publicly exposed. I have discovered several such misconfigured JIRA accounts in hundreds of companies. Some of the companies were from Alexa and Fortune top list including big giants like NASA, Google, Yahoo, etc and government sites.”

— Avinash Jain, Security Engineer

Organisations Affected

Named organisations reported to have been affected at the time of discovery included Google, Yahoo, NASA, Lenovo, 1Password, Zendesk, and a number of government bodies worldwide. Jain responsibly disclosed his findings to affected parties. Notable acknowledgements he received include:

• NASA: A previously discovered misconfigured Jira server exposed the names and email addresses of approximately 1,000 users. Jain reported this responsibly and was recognised by NASA.
• United Nations: The UN acknowledged Jain’s disclosure and recognised his role in improving their security posture.
• CODIX: A financial software solution used by European Union institutions and agencies, which also recognised Jain’s responsible disclosure.

Why This Matters: Reconnaissance and Attack Surface

Leaking employee names, email addresses, and project details may not seem as immediately dangerous as exposing passwords or financial records, but this data is highly valuable for attackers in the early stages of an attack:

• Spear phishing: Knowing which employee works on which project — and their email address — enables highly targeted phishing emails that are far more convincing than generic attempts.
• Social engineering: Role and project information helps attackers impersonate colleagues or vendors credibly.
• Competitive intelligence: Exposed project names, sprint goals, and task descriptions can reveal product roadmaps and strategic initiatives to competitors.
• Credential stuffing preparation: A validated list of real email addresses from an organisation can be fed directly into automated credential stuffing attacks against other services.

How to Audit and Fix Jira Visibility Settings

Jira administrators should review the following settings to close these exposure paths:

1. Review Global Permissions

Navigate to Settings → System → Global Permissions. Check whether “Browse Users” or “Use Jira” permissions are granted to “Anyone (including anonymous users).” Remove anonymous access unless there is a documented and deliberate reason to allow it.

2. Audit Filter and Dashboard Sharing

Review all shared filters and dashboards. Any item shared with “Public” or “All” should be reassessed. Change sharing to specific groups or projects rather than organisation-wide or public defaults.

3. Disable Anonymous Access to Projects

For each project, check Project Settings → Permissions. Ensure that “Browse Projects” is not granted to “Anyone” unless the project is intentionally public-facing.

4. Restrict User Picker Access

The user picker API endpoint can enumerate all users on an instance when anonymous access is enabled. Restricting anonymous access at the Global Permissions level prevents unauthenticated enumeration.

5. Use Atlassian’s Built-in Security Advisor

Jira Cloud includes a Security Advisor tool under site administration that flags common misconfigurations, including overly permissive anonymous access settings. Run this regularly as part of routine security reviews.

Key Takeaways for DevOps and Platform Teams

• Default settings in SaaS tools are not always secure by default — always validate permission configurations after initial setup and after major upgrades.
• “Visible to all” in a cloud tool frequently means the entire internet, not just your organisation. Read documentation carefully.
• Regularly audit third-party project management and collaboration tools as part of your organisation’s attack surface management programme.
• Google Dorks are a free, low-skill method attackers use to find misconfigured systems. If an attacker can find your Jira instance this way, you will want to know before they do.
• Responsible disclosure by external researchers is valuable — organisations should have a clear vulnerability disclosure policy and acknowledge reports promptly.