How do I find hardcoded secrets in my codebase?

Why This Matters for Security Teams

Hardcoded secrets are not just a code quality issue. They are an identity exposure problem that can turn one commit, build step, or pasted snippet into direct access to cloud accounts, source control, CI/CD, and SaaS systems. GitGuardian’s The State of Secrets Sprawl 2026 found 28.65 million new hardcoded secrets in public GitHub commits in 2025, showing how quickly leakage scales when detection is not built into delivery workflows.

Teams often miss secrets because they look only at the latest branch, while attackers and accidental exposure frequently live in full git history, pipeline variables, container layers, configuration files, and collaboration tools. That is why current guidance suggests pairing scanning with revocation and tighter credential lifecycle controls, not treating scanning as a one-time hygiene task. The OWASP Non-Human Identity Top 10 is also relevant here because every leaked secret is effectively an unmanaged non-human identity with access rights attached.

For a broader view of where these exposures originate, see NHIMG’s Guide to the Secret Sprawl Challenge. In practice, many security teams encounter hardcoded secrets only after a pipeline alert, a suspicious login, or an external disclosure has already confirmed the exposure.

How It Works in Practice

Finding hardcoded secrets requires layered detection across the software delivery lifecycle, not just regex scans of application code. A practical program scans source repositories, full git history, pull requests, CI/CD variables, container images, build artifacts, and infrastructure-as-code. It also checks for secrets that hide in YAML, JSON, Terraform, Helm charts, Dockerfiles, and deployment scripts. NHIMG’s Ultimate Guide to NHIs - Static vs Dynamic Secrets is useful here because the real control objective is not only detection, but replacing static credentials with shorter-lived alternatives wherever possible.

Effective programs combine three layers:

• Pre-commit and pre-receive scanning to stop obvious leaks before they merge.
• Repository and history scanning to catch legacy exposures already in the codebase.
• CI/CD and artifact scanning to catch secrets injected during build, test, or packaging.

The operational response matters as much as the scan. A found secret should be treated as compromised, classified by privilege and blast radius, and rotated or revoked immediately. For build-system exposure patterns, NHIMG’s CI/CD pipeline exploitation case study shows how pipeline access can become a launch point for broader compromise, which is why pipeline logs, runners, and variables deserve the same scrutiny as application code. The OWASP Non-Human Identity Top 10 reinforces this by treating exposed machine credentials as first-class identity risk rather than incidental leakage.

These controls tend to break down in monorepos with noisy build output and weak secrets ownership because alerts arrive faster than teams can confirm which credential was actually exposed.

Common Variations and Edge Cases

Tighter secrets scanning often increases alert volume and developer friction, so organisations have to balance fast feedback against false positives and build latency. That tradeoff is real, especially in large repositories, multi-language stacks, and environments where generated files, test fixtures, or sample configs resemble real credentials. Current guidance suggests tuning scanners to suppress known-safe test values while preserving high-signal detections for production paths.

One common edge case is private repositories. Teams sometimes assume private means safe, but NHIMG research consistently shows that internal code and operational content can be more exposed than public code. Another edge case is collaboration tooling: secrets pasted into tickets, chat threads, or design docs may evade code scanners entirely. For related exposure patterns, NHIMG’s 52 NHI Breaches Analysis helps explain why machine credentials so often fail at the handoff between development and operations, while the IOS app secrets leakage report shows how mobile builds can leak API keys through client-side packaging and debug artifacts.

There is no universal standard for exactly where to set scan thresholds, but best practice is evolving toward continuous scanning plus automated secret issuance, expiration, and revocation. In older systems, long-lived keys, shared service accounts, and manual exception handling make hardcoded secrets hard to eliminate completely, so security teams need a phased cleanup plan rather than a single remediation sprint. In practice, the hardest failures appear in legacy pipelines and developer laptops where secrets are copied for convenience and never return to governance.

Related resources from NHI Mgmt Group

• What is secrets sprawl and why does it create security risk?
• What is a secrets vault and which tools are available?
• How do I implement secrets scanning in a CI/CD pipeline?
• What does the hardcoded credential in a Docker image breach scenario teach us?