AI Slop Code Risk Monitor

tinyauthapp/tinyauth

Name: tinyauthapp/tinyauth — Go Code Quality & Security Review | AI Slop Code Risk Monitor
Creator: AI Slop Code Risk Monitor

Adjusted legacy signal; badge bands: 25 / 50 / 75 (different from dashboard bands).

The tiniest OpenID Certified™ authorization and authentication server you have ever seen.

0.5 Low pattern signal View on GitHub

Open-Source Code Quality, Security and Technical Debt Review

This report summarizes scanner evidence for tinyauthapp/tinyauth, a Go project with 8,444 GitHub stars. SynthScan examined 38,698 lines of code across 250 source files, recording 11 pattern matches distributed across 3 syntactic categories. The legacy adjusted pattern signal is 0.5 (Low pattern signal); it is not an authorship estimate.

Use the findings to ask whether maintainers can explain intent, verify dependencies, simplify unnecessary scaffolding, and focus review effort. Risk Payload v1 adds ownership, supply-chain, over-engineering, and review-effort vectors when available. These are prompts for human verification, not probabilities, security findings, or measured maintenance costs.

0.5

Adjusted Signal

0.5

Raw Pattern Score

100%

Time Factor

2026-10-31

Last Push

8.4K

Stars

Language

38.7K

Lines of Code

250

Files

Pattern Hits

2026-10-31

Scan Date

0.01

HC Hit Rate

Risk Review · Payload v1

Use each vector to guide manual verification of the underlying findings.

Payload schema: 1.0.0 · Engine: 2.0.0 · Scoring: heuristic-density-v1 · Scan date: 2026-10-31.

Cognitive Ownership Risk 1.5/100 · LOW

Can maintainers explain intent, assumptions, and trade-offs? 3 finding(s).

Hallucinated Dependency & Supply Chain Risk 1.0/100 · LOW

Are imports declared and package/API names verified? 8 finding(s).

Agent Over-Engineering Ratio 0.0/100 · LOW

Are wrappers, guards, and repeated structures necessary? 0 finding(s).

PR Review Velocity Impact 2.9/100 · LOW

Which changes need focused human review? 11 finding(s).

What These Metrics Mean

Adjusted Signal: Legacy pattern score normalized per 1,000 scanned lines and multiplied by the repository-level time factor. It is not an estimate of AI authorship.
Raw Pattern Score: Sum of severity- and context-weighted pattern findings before applying the time factor.
Time Factor: A coarse multiplier based on repository last-push metadata. It does not identify when individual code was written.
Pattern Hits: Count of configured pattern findings. Review each location and surrounding code before acting.
HC Hit Rate: High- and critical-severity findings per scanned file. Severity is a configured detector weight, not proof of a vulnerability or authorship.
Lines / Files: Scan coverage denominators used by the legacy normalized score.

Pattern-Signal History

This chart tracks the legacy adjusted pattern score across scan runs. Changes can reflect source changes, scan coverage, or detector/scoring versions; they do not measure AI adoption or prove that a team refactored code. Compare runs only when their scoring versions are compatible. The dashed line tracks the raw pattern-hit count.

Severity Breakdown

Groups findings by configured severity weight. Severity is not a confidence estimate, security rating, or proof that a package is missing. Verify imports against the project's manifests and inspect each finding in its local design context.

CRITICAL 0HIGH 2MEDIUM 1LOW 8

Directory Score Breakdown

This horizontal bar chart shows where configured pattern scores accumulate across directories. Treat the bars as locations to inspect, not as evidence of authorship or code quality. Use the file-level findings to decide whether a directory needs a focused ownership, dependency, complexity, or review check.

Pattern Findings

The scanner identified 11 distinct pattern matches across 3 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a detector match to inspect in context. Expand any category row to inspect the individual file paths, line numbers, code snippets, and the lexical context (CODE, COMMENT, or STRING) in which each match was detected.

Reading the findings table: The Severity column indicates the configured finding weight (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — legacy comment-context matches receive a ×1.5 score weight. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier under the legacy scoring model.

Synthetic Comment Markers2 hits · 10 pts

Severity	File	Line	Snippet	Context
HIGH	AI_POLICY.md	18	- All usage must be clearly labeled: Any content generated by AI must be clearly labeled as such. In the case that a	CODE
HIGH	AI_POLICY.md	21	- No generated content other than text is allowed: Images, videos, audio and any other form of content generated by	CODE

Fake / Example Data8 hits · 8 pts

Severity	File	Line	Snippet	Context
LOW	internal/utils/string_utils_test.go	55	assert.Equal(t, "user@example.com", utils.CompileUserEmail("user@example.com", "example.com"))	CODE
LOW	internal/utils/string_utils_test.go	58	assert.Equal(t, "user@example.com", utils.CompileUserEmail("user", "example.com"))	CODE
LOW	internal/controller/context_controller_test.go	86	Name: "John Doe",	CODE
LOW	internal/controller/context_controller_test.go	101	Name: "John Doe",	CODE
LOW	internal/service/oidc_service_test.go	37	Formatted: "123 Main St",	CODE
LOW	internal/service/oidc_service_test.go	38	StreetAddress: "123 Main St",	CODE
LOW	internal/service/oidc_service_test.go	163	assert.Equal(t, "123 Main St", info.Address.Formatted)	CODE
LOW	internal/service/oidc_service_test.go	164	assert.Equal(t, "123 Main St", info.Address.StreetAddress)	CODE

Self-Referential Comments1 hit · 2 pts

Severity	File	Line	Snippet	Context
MEDIUM	gen/docs/gen_env.go	113	buffer.WriteString("# This file is automatically generated by gen/gen_env.go. Do not edit manually.\n\n")	CODE