What are the signs that code review is failing to catch secrets in code?

Code review is failing when sensitive strings keep appearing in commits, secrets are found after merge, or reviewers repeatedly miss configuration files and scripts. Other warning signs include noisy false positives, rushed reviews, and no specialised scanning in the pipeline. If teams rely on manual inspection alone, they usually discover secrets only after exposure has already occurred.

Why This Matters for Security Teams

Secret exposure is often discovered too late to be a clean code-review problem, because review is only one control in a chain that includes commit hygiene, developer habits, and automated detection. If sensitive strings keep reaching pull requests, the review process is usually absorbing work it was never designed to carry alone. The practical question is not whether reviewers care, but whether they can reliably spot secrets hidden in long diffs, generated files, configuration blobs, or copied snippets.

That matters because leaked secrets usually create immediate blast radius: access to cloud resources, CI/CD systems, internal APIs, or deployment pipelines. Once a secret has been merged, the team is already in rotation, revocation, and incident-response mode. The most useful warning signs are therefore operational, not theoretical: repeated misses in the same file types, recurring post-merge discoveries, and a backlog of “accepted” false positives that train reviewers to ignore alerts. The average time to remediate a leaked secret is 27 days, even though 75% of organisations say they are confident in their secrets management. That gap is a strong signal that manual review confidence often exceeds actual control performance. In practice, teams usually notice the failure only after exposure, not while the review is still in progress.

How It Works in Practice

A review process is failing when it cannot consistently distinguish real secrets from ordinary code patterns, or when reviewers do not have enough time and context to inspect likely hiding places. The failure usually shows up in a few recurring ways:

Secrets appear in commits despite “careful review” because reviewers focus on logic changes and skim support files.
Tokens and keys are found in places that are easy to overlook, such as environment files, scripts, test fixtures, and deployment manifests.
The same secret types recur because the team is not learning from prior misses or feeding findings back into review checklists and automation.
Reviewers start trusting scan noise or approval culture more than evidence, so a clean review becomes a procedural event rather than a detection control.

The control usually becomes weak when review is asked to do all of the detection work. Manual inspection is best at spotting obvious mistakes in small diffs, but it degrades quickly as repositories grow, file formats diversify, and changes are compressed into hurried approvals. That is why specialised scanning in the pipeline matters: it catches the high-volume, high-repetition cases that humans miss. The problem is especially visible in organisations where developers are not consistently following secrets-handling practices, because the review process then has to compensate for upstream behaviour as well as its own limits. Only 44% of developers are reported to follow security best practices for secrets management, which helps explain why review alone often looks better on paper than it performs in production. For deeper context on the broader failure pattern, see Guide to the Secret Sprawl Challenge.

This guidance tends to break down in fast-moving release trains where reviewers are approving large generated diffs, vendored dependencies, or machine-produced configuration at speed, because the review surface becomes too broad for reliable manual detection.

Common Variations and Edge Cases

Tighter review rules often increase friction, so teams have to balance faster merges against deeper inspection of high-risk content. The main nuance is that not every missed secret means the review culture is broken; sometimes the real issue is that secrets are hard to recognise without pattern matching, context, or file-aware controls.

A few edge cases deserve special attention. First, false positives can be as damaging as false negatives if they train reviewers to dismiss alerts or rush approvals. Second, repositories with many generated files, templates, or copied snippets can hide real secrets in plain sight because the content looks “expected.” Third, teams that rely heavily on approvals from generalist reviewers may miss secrets that a more specialised workflow would catch, especially when the secret is embedded in infrastructure-as-code or deployment material rather than application logic.

Current guidance suggests treating repeated post-merge discovery as a stronger failure signal than a single missed item. One miss may be human error; a pattern of misses means the control design is weak. If the organisation can only find secrets after merge, code review should be treated as a supporting control, not the primary detection layer. For a broader set of failure patterns and remediation lessons, 17,000+ Secrets Exposed in Public GitLab Repositories is a useful reference point.

Standards & Framework Alignment

This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.

OWASP Non-Human Identity Top 10 address the attack and risk surface, while CIS Controls v8 and NIST CSF 2.0 set the governance and control requirements practitioners need to meet.

Framework	Control / Reference	Relevance
CIS Controls v8	6 — Access Control Management	Secrets in code create access-control exposure when leaked credentials remain usable.
	16 — Application Software Security	Code review failures are software-security gaps in the SDLC and review workflow.
Recommendation — Remove exposed credentials quickly and revoke any access they grant. Add secure code review and automated scanning to the development pipeline.
NIST CSF 2.0	PR.DS — Data Security	Secrets are sensitive data that must be protected in code and pipelines.
	DE.CM — Continuous Monitoring	Repeated missed secrets indicate the monitoring control is not catching exposures early.
Recommendation — Protect secrets with detection, minimisation, and controlled handling across the SDLC. Continuously monitor repositories and CI/CD output for leaked secrets.
OWASP Non-Human Identity Top 10	NHI-01 — Secrets and Credential Management	Code-review misses often expose credentials, tokens, and API keys.
Recommendation — Scan code for secrets and rotate any credentials that appear in source control.

Practitioner Guidance

What to prioritise: Prioritise the files and change types that repeatedly defeat human review, especially environment files, scripts, manifests, and copied configuration. If the same paths keep producing misses, that is a control-design problem, not an individual reviewer problem.

What to verify: Verify that secrets scanning runs before merge and that reviewers see clear, low-noise results. If the team cannot show which findings were real, which were false positives, and which were dismissed, the review process is not giving trustworthy assurance.

Decision rule: If a secret is found after merge more than once, treat manual review as insufficient on its own and add automated detection plus mandatory rotation/revocation steps. If findings only appear in a narrow file class, tune the workflow for that class rather than asking reviewers to “be more careful.”

Practitioner takeaway: The real test is not whether reviewers can occasionally spot a secret, but whether the process reliably finds secrets before they become an incident trigger.