Public report — grid, published 7 Aug 2026.
Concrete security findings (CVE IDs, secret matches, dependency versions) are hidden in this version;
ask the repo owner for the full report.
166findings with an exact file:lineof 173 — the remainder are repo-wide signals (a dimension-level measurement, not a single line); open any file:line and verify
47/102dimensions across the health lenses — wide & deep
Executive summary
Read through the Production lens — the standard calibration. *Green* means good enough to run in production. The score is absolute and comparable across repos.
guardian/grid carries serious gaps (36%). Several issues below can materially affect correctness, security, or the cost of changing it — and propagate to everything that depends on it.
Most urgent: a critical security exposure was detected (see the Security & Compliance lens). Treat it as a priority regardless of the overall grade.
The area that most needs attention is Readiness (17%) — releases are harder to depend on — versioning, release notes and dependency hygiene are thin, so consumers can't easily tell what changed or trust an upgrade. Code Health (49%) is the next concern — changes there are slower and more error-prone.
Leadership focus, highest impact first: ILogger (or Serilog) and log at meaningful points across… (Observability); Codify backups + geo-recovery in IaC and document RTO/RPO… (DR & Backup); tests that import the unreached modules (directly or through… (Test Coverage).
For scale: — (~0 production lines); rebuilding it from scratch would take roughly — (—). Approximate, ±~30%.
How the score is built — each lens's share of the headlineWidth is the lens's weight in the worst-heaviest fold (the weakest area pulls hardest); colour is that lens's own band. A lens fixes the score in proportion to its width.
Of everything flagged, the best return on effort is: Adopt ILogger (or Serilog) and log at meaningful points across the projects. The rest can wait behind it.
Evidence: priority ranking: top of 5 ranked by impact/effort
→ Adopt ILogger (or Serilog) and log at meaningful points across the projects.
At a glance — Code Health · 49% · Weak · gated by D4, R1, R7
Findings mapped to OWASP categories; the specific CVEs/secrets are in the Security dimension cards below and findings.md (redacted only on the public version of this report).
OWASP category
Findings
Severity
A03:2021 — Injection
50
High / Critical
A06:2021 — Vulnerable & Outdated Components
50
High / Critical
A02:2021 — Cryptographic Failures
2
High / Critical
A05:2021 — Security Misconfiguration
2
High / Critical
Roadmap
First, implement structured logging across all projects to establish baseline observability. Next, codify disaster recovery and backup procedures in infrastructure as code to ensure data resilience. Then, expand test coverage to include all unreached production modules. Finally, clean up dependencies by removing unused packages and explicitly declaring imports, while adding static analysis and security scanning to the build process.
Ranked by impact ÷ effort. "Helps" is the estimated gain on the 0–100 health score.
Do this
Helps
Effort
Dimension
Resolve the 1 No automated tests finding(s) in Code Coverage.
Watchdog is a deep, periodic assessment — run each sprint, monthly, or quarterly, taking the time to go wider and deeper than a quick check and surfacing in one coherent report what you'd otherwise piece together from a dozen separate tools. It scores deterministically: the same commit yields the same score, every run. 44 of 47 evaluated dimensions are computed purely by tools and static analysis (confidence 1.0); 3 documentation/naming judgement(s) are LLM-assisted and labelled advisory. Overall confidence is 0.7 — the weighted average across measured dimensions; it falls as more of the score leans on LLM-assisted judgement and rises when it's fully tool-backed.
Every figure here is one of three kinds, and we label which: ✓ Measured — a deterministic fact (LoC, complexity, coverage); ~ Modeled — an estimate from a stated model (cost, effort, value-at-risk), always a range with its assumptions, never a precise fact; ◐ Advisory — an LLM prose judgement. We never present a modelled estimate as if it were measured. Perfect or absent scores carry their provenance too (ADR-0011): ✓ Tool-verified means the property itself was measured across the surface; ○ Nothing flagged means the probes came back clean — a claim bounded by what a repository can show; ⊘ Not evidenced means a working control (a tested restore, an automated rollback) showed no positive evidence — absence of evidence is not evidence of a control, so it's excluded from the score rather than awarded a spurious 10; ◐ Sampled · advisory marks an LLM verdict over a bounded sample — advisory, never a deterministic measurement.
What we checked — 47 dimensions across the health lenses
Each chip is a dimension scored from real signals across architecture, testing, dependencies, security & compliance, documentation, git-history and code quality — in one coherent pass. A surface report typically covers a handful.
How to trust any code-health report — three questions
Can you open the finding? Real findings cite a repo-relative file and line you can open at the cited line — never an absolute scratch path. Here, 166 of 173 do; the remainder are repo-wide signals — a dimension-level measurement, not a single line. (Every path in this report is repo-relative by construction: paths are normalized at the producer and the report is rejected if any rooted path leaks through.)
Is there a tool behind the number? Every score below names the method that produced it — Roslyn, git, a scanner, or (for a handful of documentation/naming dimensions) an LLM labelled sampled · advisory — not a narrative.
Does re-running give the same result? Run it again on the same commit and the score — and this report, byte for byte — is identical. A report whose numbers move between runs is describing the run, not the code.
This report answers yes to all three. That's the bar to hold any assessment to.
Tools & methods
The actual versions used this run (captured at analysis time) — re-run on the same commit for the identical score.
Method
Backs
Version
Evaluator
Roslyn static analysis
Complexity, cohesion, coupling, dead code, API surface, layering
What ran differently this time — a tool absent, degraded, or that fell back to an estimate. Named openly, not folded silently into the scores. A degraded run also records its exact cause in diagnostics.md.
D18 Solution Shape — evaluation did not complete — Dimension evaluation failed — excluded from the score.
D19 Documentation Quality — LLM provider failed — The model provider returned an unusable result, so this LLM-assisted dimension fell back to a measurement gap (confidence 0) rather than a penalty. Re-run with a reachable provider to score it.
D30 Dependency Vulnerabilities — scanner not present in this environment — The backing tool was not installed where this scan ran, so this dimension was not scored. Install the tool (or run in the hosted environment, where it is always present) for a graded result.
D32 Data Compliance (PII/GDPR) — scanner not present in this environment — The backing tool was not installed where this scan ran, so this dimension was not scored. Install the tool (or run in the hosted environment, where it is always present) for a graded result.
D36 Supply-chain Provenance & Signing — scanner not present in this environment — The backing tool was not installed where this scan ran, so this dimension was not scored. Install the tool (or run in the hosted environment, where it is always present) for a graded result.
D37 Vulnerability-disclosure Policy — scanner not present in this environment — The backing tool was not installed where this scan ran, so this dimension was not scored. Install the tool (or run in the hosted environment, where it is always present) for a graded result.
Repo exclusion declarations (.gitattributes linguist-generated/vendored, .editorconfig generated_code): none declared — every source file was scored.
Limitations & what we did not check
Watchdog assesses the repository exactly as committed, and only the repository. By design it does not reach outside the source tree: the live cloud account, the running CI/CD pipeline, the host's branch-protection and approval rules, the production configuration, or a restore actually exercised against a backup are all out of scope. That boundary is a feature, not a gap — a repo-relative, deterministic scan re-runs identically on any commit and every finding opens at a real file and line, where a live audit can neither be reproduced nor traced. The visible consequence is that controls which leave no in-repo evidence are reported as "not evidenced" and excluded from the score rather than awarded a number a static scan cannot justify.
Per-dimension blind spots
For each dimension that was measured, what a static, repo-only scan structurally cannot see — the honest edge of the measurement, not a failure of it.
D1 Cyclomatic Complexity: Cyclomatic complexity counts branches statically — it cannot tell an essential decision tree from accidental tangle, nor see complexity that lives in data or configuration (large switch-case token tables, DSL lexers/parsers, data-as-code rule tables) rather than control flow: a tokenizer's many single-character cases read as high complexity though each branch is trivial.
D2 Cognitive Complexity: Cognitive-complexity heuristics approximate how hard code is to follow; genuine domain difficulty and well-named intent that eases reading are not captured.
D3 God Classes: "God class" is sized by members and responsibilities visible in the type — a deliberately broad facade over a coherent subsystem can read the same as an accidental grab-bag. For front-end JS the file-length check is cohesion-aware (a single-responsibility module — one class/IIFE — earns a 3× threshold), but cohesion is approximated from top-level declarations, not true dependency structure.
D4 Code Duplication: Duplication is token-similarity (jscpd) — it finds copy-paste, not semantic duplication expressed differently. Committed machine-written code (EF migration scaffolds, *.Designer.cs, model snapshots) is EXCLUDED — its repetition is the tool's, not the team's — so the score reflects hand-written duplication only; the generated footprint is reported separately under Solution Shape.
D5 Coupling: Coupling is measured between projects/assemblies — runtime coupling through DI, reflection, messaging or shared databases is invisible to a static reference graph.
D7 Architectural Integrity: Layering is checked against detected/declared rules — an architecture whose boundaries live in convention or in code review, not in a rule a scanner can read, is not enforced here.
D8 Code Coverage: Coverage is measured by building and running the suite (`dotnet test --collect`) inside Watchdog's isolated image — the target repo is never modified, and nothing on your systems runs. So coverage exists only when the suite builds and runs within the inline time budget; one that needs external services, can't build, or exceeds the budget yields no coverage (D8 then degrades to not-measured, not a low score). Line coverage also says nothing about assertion quality.
D9 Test Distribution: The test-pyramid shape is inferred from project/folder naming and references, with a single test host bucketed per-file by its path tier and content signals — a suite that names tiers unconventionally and gives no per-file signal can still be mis-bucketed.
D12 Dependency Hygiene: Dependency health reads manifests and lockfiles — a vulnerability in a vendored/copied dependency, or risk from how a dependency is actually used, is outside this view.
D13 Secret Scanning: Secret detection is signature- and entropy-based on the current tree — a secret that does not match a known pattern, or one already rotated, will not be flagged (a clean scan is "nothing matched", not "no secrets exist").
D15 Churn × Complexity Hotspots: Churn hotspots come from git history — a freshly imported or squashed repository has no churn signal, and recent rewrites can mask a historically risky file.
D16 Bus Factor: Bus-factor is a time-decayed model of commit attribution (who has recently, repeatedly worked a file), not comprehension — pairing, review and reading-without-committing spread knowledge it can't see; bot commits and shared accounts still distort it.
D20 ADR Quality: ADR quality is an LLM read of the decision records present — it cannot know about decisions made and never recorded, and its verdict is sampled and advisory.
D21 Naming Consistency: Naming quality is an LLM judgement over a bounded sample — it assesses clarity/consistency of the names it sees, not domain-correctness, and is advisory.
D25 ADR Conformance: ADR conformance is the LLM-scored fraction of sampled code that follows recorded decisions — it checks the decisions that were written down and the slices it sampled, not unrecorded rules or the whole tree.
D26 Project Cohesion: Project focus is sized from members/namespaces per project — a project that is broad by deliberate design reads the same as one that has sprawled.
D27 Navigability: Indirection/navigability is structural — it measures hops to follow a call, not whether that indirection buys real flexibility or just ceremony.
D28 Secrets (history): Secret-history scanning sweeps the git log for known patterns — a secret that predates the available history, or never matched a signature, is not found (clean means "nothing matched in the history we can see").
D29 Static Analysis (SAST): SAST findings are pattern-based (semgrep) — it finds classes of bug it has rules for; logic flaws, auth/authorization gaps and issues needing runtime context are out of reach (and clean means "no rule matched").
D31 IaC & Container Security: IaC scanning checks Dockerfiles/Terraform/Kubernetes against best-practice rules — it cannot see the live cloud account, runtime configuration, or drift between the committed config and what is actually deployed.
D33 JS/npm Dependency Vulnerabilities: JS/npm CVE matching reads package manifests and lockfiles — risk from how a dependency is used, and advisories not yet published, fall outside this scan.
D34 Knowledge Freshness: Freshness is decayed commit RECENCY, not comprehension — code read often but rarely committed reads as orphaned, and stable code that genuinely needs no changes is penalised the same as forgotten code; bot/squash commits distort it like the bus factor.
D35 Change Coupling: Change coupling is co-change in COMMITS — files split across separate commits, or coupled only through a shared config/build step, read as uncoupled, and a sweeping commit (rename/format) is excluded so it doesn't couple everything. It shows that files change together, not WHY: a high coupling can be a healthy cohesive pair as readily as a hidden leak.
M4 Documentation accuracy: Onboarding quality is an LLM read of the docs/setup present — it cannot run the onboarding or measure how long a real new joiner takes; the verdict is sampled and advisory.
P4 Deployment & Rollback: Approval/branch-protection rules live in repository settings the scan cannot see — only their in-repo evidence (config files, workflows) is checked, so a control enforced purely in the host's settings reads as "not evidenced".
P5 DR & Backup: Backup/restore and disaster-recovery readiness is judged from in-repo evidence — a config that exists is not a tested restore, so the absence of positive evidence is reported as "not evidenced", never scored as present.
The LLM boundary
LLM-set scores this run (4): D20, D21, D25, M4 (model: Local LLM). For these, a model reads a bounded sample and sets the numeric score (documentation, ADR quality, naming, comment value, onboarding) — D25 sets the ADR-conformance fraction over sampled code, D22 judges API accuracy over a sample. These are sampled and advisory by design: they vary at the margins between runs and are never a deterministic measurement. Every other score in this report is tool-computed at confidence 1.0.
What it measures: How tangled the control flow is — methods with many branches are hard to test and change.
Method: Cyclomatic complexity per method (1 + decision points), computed exhaustively across production source; test projects separated by convention. Deterministic.
Resolve the 1 watchSearchChange (cyclomatic 22) finding(s) in Cyclomatic Complexity — start with query.js. — One of this dimension's main actionable groups (1 warning-level).
Detailed fixes: d1_recommendation.md · top locations in Appendix A, every location in findings.md.
What it measures: How hard the code is for a person to follow, beyond raw branching.
Method: Cognitive complexity per method (Sonar-style nesting-penalized score), computed exhaustively over production code, excluding test projects. Deterministic.
Resolve the 1 watchSearchChange (cognitive 26) finding(s) in Cognitive Complexity — start with query.js. — One of this dimension's main actionable groups (1 warning-level).
Resolve the 1 manageUploadedBy (cognitive 20) finding(s) in Cognitive Complexity — start with query.js. — One of this dimension's main actionable groups (1 warning-level).
Resolve the 1 (anonymous) (cognitive 19) finding(s) in Cognitive Complexity — start with query.js. — One of this dimension's main actionable groups (1 warning-level).
Detailed fixes: d2_recommendation.md · top locations in Appendix A, every location in findings.md.
Do you agree with this assessment?
D3 · God Classes10.0 / 10Exemplary✓ Tool-verified
What it measures: Over-large classes that try to do too much ("god classes").
Method: God-class detection by line and method-count thresholds per logical type (partial classes unified), filtered for generated code and registration/contract false positives. Deterministic.
What it measures: Copy-pasted code that should be shared instead.
Method: Code duplication via token-stream sliding windows with type-aware normalization (locals masked, type names preserved), density-scored per KLoC of production code. Deterministic.
+ 7 more group(s) — more in Appendix A; the complete list is findings.md.
What to do
Resolve the 9 Duplicated block (5 lines × 2) finding(s) in Code Duplication — start with ConfigDocumentParser.java (2), ConfigDelayedMerge.java, ConfigImplUtil.java. — One of this dimension's main actionable groups (9 warning-level).
Resolve the 5 Duplicated block (6 lines × 2) finding(s) in Code Duplication — start with SimpleConfig.java (2), SimpleConfigOrigin.java (2), ConfigDocumentParser.java. — One of this dimension's main actionable groups (5 warning-level).
Resolve the 2 Duplicated block (13 lines × 2) finding(s) in Code Duplication — start with ResolveSource.java, SimpleConfigList.java. — One of this dimension's main actionable groups (2 warning-level).
Enforce Code Duplication in CI to reach Verified (currently Documented). — Hardens enforcement from Documented toward Verified — provenance only; does not change the score.
Detailed fixes: d4_recommendation.md · top locations in Appendix A, every location in findings.md.
Do you agree with this assessment?
D5 · Coupling6.9 / 10Adequate✓ Tool-verified
What it measures: Whether volatile projects sit underneath others that depend on them (so their churn ripples upward), and whether project dependencies form cycles. A widely-depended-on but stable shared/kernel project is healthy, not penalised.
Method: Dependency cycles via elementary-DFS over real .csproj references, plus Martin instability (afferent/efferent) per project. Exhaustive over the reference graph, deterministic.
Coverage: Exhaustive · type-level: afferent/efferent coupling + cycles computed over every production type — the population is all types, not a name convention.
Enforce Coupling in CI to reach Verified (currently Documented). — Hardens enforcement from Documented toward Prevented — provenance only; does not change the score.
What it measures: Whether the code respects its intended layering / architecture rules.
Method: Enforcement rung (Prevented/Verified/Documented) per checkable ADR via Roslyn, plus dependency cycles via the engine shared with D5/AX3. Deterministic, exact.
Of 5 mechanizable ADRs, 4 are prevented by analyzers, 1 by tests, 0 exist only in prose. Coverage: 100 %. Cycles found: 0.
✓ On the Gold path — maintain.
Detailed fixes: d7_recommendation.md.
Do you agree with this assessment?
D8 · Code Coverage0.0 / 10Critical✓ Tool-verified
What it measures: How much of the code is actually exercised by tests.
Method: Coverage from coverlet runs or committed reports (Cobertura/OpenCover/lcov), computed per-file with structured exclusions for generated, trivial, and glue code. When the suite can't be built/run in-image AND no report is committed, coverage is reported NOT-MEASURED (excluded from the score) with the precondition to make it measurable — never a LoC-ratio proxy folded in as if measured. Deterministic.
No automated tests — the solution has no test code.
No automated tests
What to do
Resolve the 1 No automated tests finding(s) in Code Coverage. — One of this dimension's main actionable groups (1 issue-level).
Enforce Code Coverage in CI to reach Verified (currently Documented). — Hardens enforcement from Documented toward Verified — provenance only; does not change the score.
Detailed fixes: d8_recommendation.md · top locations in Appendix A, every location in findings.md.
Do you agree with this assessment?
D9 · Test Distribution0.0 / 10Critical✓ Tool-verified
What it measures: Whether the test suite has a healthy mix of unit / integration / end-to-end tests.
Method: Test projects classified (Unit/Integration/BDD/E2E) from compiled metadata; test methods counted exhaustively across projects with placement-agnostic disk fallback. Deterministic.
What it measures: Whether dependencies are current, secure, and not bloated.
Method: Manifest scan via dotnet list package across all projects; worst-signal-per-package deduction (saturating for vulnerabilities, capped-linear for deprecation/outdated) per KLoC. Exhaustive, deterministic.
What it measures: Whether any secrets (keys, tokens, passwords) have leaked into the code.
Method: In-process native secret scanner (entropy plus signature patterns) across all tracked files; no external tool. A clean result is a measured 10, not no-data zero. Deterministic.
What it measures: Files that change often and are also complex — the riskiest hotspots.
Method: Per production file churn times cyclomatic complexity over a rolling window, computed from git and Roslyn/JS/Razor analysis. Exhaustive, deterministic per commit date.
Detailed fixes: d15_recommendation.md · top locations in Appendix A, every location in findings.md.
Do you agree with this assessment?
D16 · Bus Factor10.0 / 10Exemplary✓ Tool-verified
What it measures: Whether knowledge is concentrated in too few people (the "bus factor").
Method: Living knowledge per author via time-decayed commit attribution (6-month half-life, focus weighting) across largest source files. Deterministic, avoids blame's mechanical-refactor false positives.
What it measures: Whether architecture decisions are recorded well (context, decision, consequences).
Method: Per-ADR judgment by language model at low temperature with two-pass stability; confidence is share of ADRs evaluated; enforcement-field presence detected deterministically. Advisory.
Evaluated 35 ADR(s) individually; mean quality 5.1/10 (mixed — many ADRs miss context or consequences). 21 flagged with a specific gap.
No context/problem and no decision; only a file-structure referencedocs/00-about/02-structure.md
Decision is an install list with no context/problem and no consequences/trade-offsdocs/01-setup/01-software-dependencies.md
No context/problem and no consequences/trade-offs; only a one-line dependency list with credentials and resource linkage is presentdocs/01-setup/02-aws-dependencies.md
No consequences/trade-offs section (the host-resolve-to-127.0.0.1 requirement and the need to add each subdomain via hosts are noted but not framed as trade-offs)docs/01-setup/03-configuring-setup.md
The body is a runnable setup script with flags but the context/problem driving the decision and any consequences/trade-offs are absentdocs/01-setup/03-running-setup.md
+ 16 more group(s) — more in Appendix A; the complete list is findings.md.
What to do
Resolve the 1 No context/problem and no decision; only a file-structure reference finding(s) in ADR Quality — start with 02-structure.md. — One of this dimension's main actionable groups (1 warning-level).
Resolve the 1 Decision is an install list with no context/problem and no… finding(s) in ADR Quality — start with 01-software-dependencies.md. — One of this dimension's main actionable groups (1 warning-level).
Resolve the 1 No context/problem and no consequences/trade-offs; only a one-line… finding(s) in ADR Quality — start with 02-aws-dependencies.md. — One of this dimension's main actionable groups (1 warning-level).
Detailed fixes: d20_recommendation.md · top locations in Appendix A, every location in findings.md.
What it measures: Whether names — types, methods, variables — are clear and consistent.
Method: Judged by language model at low temperature (0.0-0.1) on a deterministic random symbol sample (fixed size, not exhaustive), with disclosed confidence band. Advisory, sampled.
What it measures: Whether the code actually follows the decisions recorded in the project's ADRs.
Method: Judged by language model at low temperature against ADRs plus a deterministic structural code summary; findings linked to repo-rooted ADR paths for traceability. Advisory.
What it measures: How far you must trace to follow a call — low indirection and co-located slices read easier.
Method: Call indirection (interface hops, cross-namespace calls, slice-locality scaled) over a sampled set of method invocations, size-aware baseline. Sampled; confidence discounted by symbol-resolution gaps.
Coverage: Slice locality from the first namespace segments, SAMPLED (≤400 methods) — not exhaustive.
What it measures: Whether any secrets were ever committed — scanned across the full git history, not just now.
Method: Git-history secret scan via gitleaks detect over full history in an isolated checkout; each match flagged High. Exhaustive; degrades cleanly when tool absent.
4 finding(s): 0 critical, 4 high, 0 medium, 0 low. Remediation for historically-committed secrets is credential rotation — they remain in history regardless of later deletion.
Secret: travisci-access-tokenmedia-api/app/lib/querysyntax/QuerySyntax.scala:286detected by gitleaks finding
Rotate the exposed credentials — git history can't be un-committed
What to do
Resolve the 1 Secret finding(s) in Secrets (history) — start with QuerySyntax.scala. — One of this dimension's main actionable groups (1 issue-level).
Resolve the 1 Rotate the exposed credentials finding(s) in Secrets (history). — One of this dimension's main actionable groups (1 recommendation-level).
Detailed fixes: d28_recommendation.md · top locations in Appendix A, every location in findings.md.
What it measures: Real static-analysis (SAST) findings — likely security bugs in the code, any language.
Method: Polyglot static analysis via semgrep across the repo using the pinned, image-baked p/security-audit + p/owasp-top-ten rulesets (no scan-time registry fetch); severity rules (ERROR/WARNING/INFO) map to a full-band severity-weighted score. Exhaustive, deterministic; degrades on parse failure.
Coverage: semgrep pattern rules over all files — exhaustive for the rule set, blind to classes of bug without a rule (clean = no rule matched).
High: dependabot-missing-cooldown · ×28.github/dependabot.yml:5detected by semgrep finding
Medium: var-in-href · ×20kahuna/public/js/common/user-actions.html:12detected by semgrep finding
Low: request-host-used · ×2common-lib/src/main/scala/com/gu/mediaservice/lib/config/Services.scala:90detected by semgrep finding
What to do
Resolve the 28 High finding(s) in Static Analysis (SAST) — start with ImageResponse.scala (9), ElasticSearch.scala (5), dependabot.yml (5). — One of this dimension's main actionable groups (28 issue-level).
Resolve the 20 Medium finding(s) in Static Analysis (SAST) — start with global.html (8), user-actions.html (3), gr-downloader.html (2). — One of this dimension's main actionable groups (20 warning-level).
Resolve the 2 Low finding(s) in Static Analysis (SAST) — start with Services.scala, nginx.conf. — One of this dimension's main actionable groups (2 recommendation-level).
Detailed fixes: d29_recommendation.md · top locations in Appendix A, every location in findings.md.
What it measures: Whether anyone still has living knowledge of each file, or it has been orphaned — last understood long ago by someone now gone quiet. The sibling of the bus factor: D16 asks who owns it, D34 asks whether anyone still knows it.
Method: File orphaning as total living-knowledge decay below one focused-commit's worth within a year, computed per-file from the D16 decay model. Exhaustive, deterministic over fixed history.
What it measures: Whether files that change together actually belong together — pairs that repeatedly co-change in git history despite having no explicit code dependency, surfacing the hidden/logical coupling (and boundaries in the wrong place) a static scan can't see.
Method: Pairwise co-occurrence over the per-commit file sets in git history (production source only — tests and generated dropped): Degree-of-Coupling = shared ÷ min individual revisions, reported above noise floors (each file ≥10 revisions, ≥5 shared commits, ≥50% strength); sweeping commits excluded. Deterministic over fixed history.
Coverage: Population: PRODUCTION source files only — test and generated files are dropped before pairing, so a class co-changing with its own test (trivially ~100%) can't drown the real production↔production coupling. Pairs ranked by Degree-of-Coupling; coupling through a build step, config, or non-source file isn't seen.
Resolve the 7 Change coupling finding(s) in Change Coupling — start with backfiller.ts, edits-api.js, embedder.ts. — One of this dimension's main actionable groups (7 warning-level).
Resolve the 3 Boundary-crossing change coupling finding(s) in Change Coupling — start with image-embedder-lambda.ts (3). — One of this dimension's main actionable groups (3 issue-level).
Detailed fixes: d35_recommendation.md · top locations in Appendix A, every location in findings.md.
What it measures: Whether dependencies have known published vulnerabilities (CVEs) per the OSV database — npm and other lockfile ecosystems, parsed natively. Complements D33 (npm via trivy) and D30 (.NET via dotnet).
Method: npm/multi-ecosystem CVE scan via osv-scanner (queries the osv.dev database + parses lockfiles natively: package-lock/yarn/pnpm/bun); severity tally (Critical/High/Medium/Low) to 0-10 tight normalizer (8.0). NotApplicable without a JS lockfile. Additive to D33 (trivy fs); exhaustive + deterministic, DB kept fresh.
High CVE: [GHSA redacted] · ×38cdk/package-lock.jsondetected by osv-scanner finding
Critical CVE: [GHSA redacted] · ×2cdk/package-lock.jsondetected by osv-scanner finding
Medium CVE: [GHSA redacted] · ×10cdk/package-lock.jsondetected by osv-scanner finding
What to do
Resolve the 38 High CVE finding(s) in OSV Dependency Vulnerabilities — start with package-lock.json (38). — One of this dimension's main actionable groups (38 issue-level).
Resolve the 2 Critical CVE finding(s) in OSV Dependency Vulnerabilities — start with package-lock.json (2). — One of this dimension's main actionable groups (2 issue-level).
Resolve the 10 Medium CVE finding(s) in OSV Dependency Vulnerabilities — start with package-lock.json (10). — One of this dimension's main actionable groups (10 warning-level).
Detailed fixes: d38_recommendation.md · top locations in Appendix A, every location in findings.md.
Other · Architecture — Whether the codebase has a recognisable, scale-appropriate structure (a named architectural style, or modular enough for its size) rather than being an ad-hoc ball of mud.
Method: Roslyn plus csproj analysis: architecture style detection (DDD, clean, vertical-slice, CQRS) and structure fitness for repo size. Deterministic.
Maturity · Maturity — Whether the repo and its projects have a README, and whether it's substantive and current.
Method: Filesystem scan: README presence, word count, and headings for depth; git history for staleness. Exhaustive across root and project dirs, deterministic.
The root README is 106 words — likely missing build/run/architecture context.
What to do
Expand the README with getting-started, architecture overview and a project map.
Add a build/run (quick start) section to the root README — the first thing a newcomer needs.
Add a 'Testing' section to the root README — how to run the test suite.
Add an 'Architecture' / 'How it works' section to the root README — the high-level shape.
Maturity · Maturity — Whether the README actually describes the code that exists (LLM-judged, advisory).
Method: Judged by language model at low temperature: README accuracy versus actual projects, within a disclosed tolerance. Advisory, not a measured number.
Grid is claimed to be the Guardian's image management system but no project in the evidence (or its subdirectories) matches this description
What to do
Reconcile the README with reality: Grid is claimed to be the Guardian's image management system but no project in the evidence (or its subdirectories) matches this description.
Readiness · Readiness — Whether SAST, secret/dependency scanning and performance benchmarking are wired in (presence, not runtime).
Method: Filesystem/Roslyn scan: CodeQL, Dependabot, secret-scanning, and BenchmarkDotNet presence in pipelines and projects. Exhaustive, deterministic.
Readiness · Readiness — Whether releases are automated and safely reversible (probes, rolling updates, approval gates) — from manifests/pipeline files, not the live environment.
Method: Filesystem scan: deployment manifests/IaC (K8s YAML, Helm, Terraform) for rolling updates, probes, approval gates, migration hooks. Exhaustive, deterministic.
Deployment automation exists but no readiness/liveness probes, rolling-update strategy, lifecycle hooks or migration job were evidenced — a bad release is harder to detect and reverse.
What to do
Add readiness/liveness probes and a rolling-update (or blue/green) strategy so a bad release is caught and rolled back automatically.
Do you agree with this assessment?
P5 · DR & Backup0.0 / 10Critical✓ Tool-verified
Readiness · Readiness — Whether disaster recovery is planned and codified — backups, geo-recovery, RTO/RPO, persistence guarantees — from IaC + container manifests + docs, never the live cloud.
Method: Filesystem scan: disaster recovery, backup, geo-recovery, RTO/RPO, persistence guarantees from IaC, manifests, and docs. Exhaustive, deterministic, never a live environment.
A persistence guard (data volume / purge-protection) was found, but no backup, geo-recovery or RTO/RPO controls were evidenced — a volume that survives a container recreate is not a tested restore from catastrophic loss.
What to do
Codify backups + geo-recovery in IaC and document RTO/RPO and the restore procedure — a persistence guard alone is not disaster recovery.
Do you agree with this assessment?
R1 · Type Safety2.7 / 10Weak✓ Tool-verified
React / JS · Code Health — How much of the frontend is typed TypeScript vs untyped JavaScript.
Method: Frontend file inventory: the share of typed TypeScript vs untyped JavaScript across the source tree. Deterministic, exhaustive over frontend files.
54 typed · 147 plain JS
What to do
Migrate the remaining .js/.jsx files to TypeScript.
React / JS · Code Health — Per-function cyclomatic/cognitive complexity from the token-level function scanner (D-386) — real branching, not a regex heuristic.
Method: Per-function cyclomatic/cognitive complexity from a token-level function scanner (real branching, not a regex heuristic), computed over every frontend function. Deterministic.
Branch-heavy code is where defects cluster — extract decisions into smaller functions. (×8) — query.js:248, syntax.ts:35, query.js:482, …
What to do
Break down the listed branch-heavy functions; aim P95 cyclomatic ≤ 5.
Do you agree with this assessment?
R3 · Large Files8.9 / 10Strong✓ Tool-verified
React / JS · Code Health — How many components/modules exceed the large-file threshold.
Method: Components/modules exceeding the large-file threshold, counted exhaustively across the frontend source tree. Deterministic.
What to do
Split the oversized components into smaller, focused ones.
Do you agree with this assessment?
R4 · Test Coverage1.3 / 10Critical✓ Tool-verified
React / JS · Readiness — Static test reachability (D-386): the share of production files reachable from any test via the import graph — measured without running anything.
Method: Static test reachability: the share of production files reachable from any test via the import graph — measured without running anything. Deterministic.
No test imports this module directly or transitively — its behavior is unverified. (×8) — results.js, gr-image-metadata.js, main.js, …
What to do
Add tests that import the unreached modules (directly or through their public entry).
React / JS · Readiness — How outdated the npm dependencies are (a maturity signal). JS/npm CVEs are scored separately in D33 (JS/npm Dependency Vulnerabilities).
Method: npm dependency staleness from manifest/registry metadata (a maturity signal; JS/npm CVEs are scored separately in D33). Deterministic.
Do you agree with this assessment?
R6 · Tooling10.0 / 10Exemplary✓ Tool-verified
React / JS · Readiness — Whether the project wires up test, lint and typecheck — detected from each package.json script's COMMAND (eslint / tsc / vitest / jest / playwright), not just its name, and corroborated against CI-workflow invocations so a tool run only in CI still counts.
Method: package.json scanned for test/lint/typecheck script wiring. Deterministic presence check.
Do you agree with this assessment?
R7 · Dead Code0.0 / 10Critical✓ Tool-verified
React / JS · Code Health — Files unreachable from every application/tooling/test entry point, and exports nothing imports (module-graph reachability, D-386).
Method: Dead code: files unreachable from every application/tooling/test entry point plus exports nothing imports, via module-graph reachability. Deterministic, exhaustive over the import graph.
Unreachable from the 11 application, 9 tooling and 14 test entry point(s) detected in this repo. Gate removals on your build/type-check — an undetected custom entry would make these reachable.
no import path from any entry point (11 application, 9 tooling, 14 test roots considered) (×7) — results.js, gr-image-metadata.js, main.js, …
What to do
Delete the dead files and unused exports — every line is maintenance cost and rebuild-estimate inflation with zero runtime value.
React / JS · Readiness — npm dependency truthfulness (D-386): unused dependencies, imports not declared anywhere, and type-/test-only packages shipped as production deps.
Method: npm dependency truthfulness: unused dependencies, imports declared nowhere, and type-/test-only packages shipped as production deps — from the manifest + import graph. Deterministic.
Imported but not declared in any reachable package.json — installs work only by hoisting accident. (×2) — embedder.ts:3, backfiller.test.ts:7
Declared in cdk/package.json but never imported anywhere in that package or its workspace members — dead weight and attack surface. Verify against build tooling before removing. (×3)
What to do
Remove unused dependencies, declare unlisted imports explicitly, and demote type-/test-only packages to devDependencies.
React / JS · Architecture — Import cycles in the module graph (D-386) — files that can only be understood and changed together.
Method: Import cycles in the module graph, detected exhaustively over JS/TS imports (the same cycle detection as the .NET coupling dimension). Deterministic.
Other · Code Health — Whether the code avoids sync-over-async (deadlock-prone blocking on tasks) and async void.
Method: Roslyn syntax scan: async methods scanned for .Wait()/.GetAwaiter().GetResult() and async-void outside event handlers. Deterministic, hard fact per invocation.
Other · Code Health — Whether exceptions are handled rather than silently swallowed or rethrown with lost stack traces.
Method: Roslyn syntax scan: every catch clause counted; empty catches and bare rethrows flagged. Population is all catch clauses, not estimated. Deterministic, hard fact.
Other · Code Health — Whether log calls use message templates (queryable) rather than interpolated strings.
Method: Roslyn syntax scan: every log call-site counted; interpolated-string first-argument violations flagged. Population is all log calls, not estimated. Deterministic.
Do you agree with this assessment?
Reference — by lens
The score is the rank-weighted fold of these lenses (worst-heaviest), each including its meta-dimensions; a lens with a Critical contributor is capped at Fair (its band reads "gated by …") and is never the strongest area however high its average.
Capped at Fair by a Critical contributor — resolve it before relying on this lens.
Not included — 56 check(s) not relevant to this codebase
These checks had nothing to measure here (no tests, no git history, the codebase is small, or the architecture style doesn't apply), so they're omitted above rather than scored low.
AC1 Text alternatives — No user-facing web UI (the repo is a library/CLI/worker/headless service) — accessibility is not applicable.
AC2 Forms & labels — No user-facing web UI (the repo is a library/CLI/worker/headless service) — accessibility is not applicable.
AC3 Page structure — No user-facing web UI (the repo is a library/CLI/worker/headless service) — accessibility is not applicable.
AC4 Keyboard semantics — No user-facing web UI (the repo is a library/CLI/worker/headless service) — accessibility is not applicable.
AC5 ARIA correctness — No user-facing web UI (the repo is a library/CLI/worker/headless service) — accessibility is not applicable.
AC6 Visual & motion safety — No user-facing web UI (the repo is a library/CLI/worker/headless service) — accessibility is not applicable.
AC7 A11y enforcement — No user-facing web UI (the repo is a library/CLI/worker/headless service) — accessibility is not applicable.
AX1 Captive dependencies — no DI registrations detected
AX10 Code composition — no source files detected — code composition not applicable
AX2 Stateful singletons — no singleton implementations detected
AX3 Project dependency cycles — no csproj graph available
AX4 Dependency direction — no csproj graph available
AX6 Interface segregation — no public interfaces
AX7 Slice cohesion — not applicable — not a vertical-slice architecture
AX8 Test isolation — no test/production split to check
AX9 CQS / query purity — no CQRS query handlers detected — query purity is not applicable to this codebase
AXB2 Runtime readiness — no data
C1 Data Protection — No personal data detected in the analyzed source — no PII-typed entity/column names (Email, FirstName, DateOfBirth, …), no ASP.NET Identity / user-account model, and no stored user credentials. GDPR data-protection controls are therefore N/A here. If this is intentional, record the no-PII posture in an ADR; if the app does process personal data, name those fields conventionally so this dimension activates.
C2 Access Controls — No access-control surface detected in the analyzed source — no web/app surface to authorize (no HTTP API or web-UI project) and no authorization code at all (no [Authorize]/policies, no imperative guard methods). Access control is therefore N/A here — this is a library/CLI, which is authorized by its CALLER, not by itself. If this codebase grows request handlers, the dimension reactivates and a default-deny posture is expected then.
C3 Audit Trail — No personal data detected in the analyzed source — no PII-typed entity/column names (Email, FirstName, DateOfBirth, …), no ASP.NET Identity / user-account model, and no stored user credentials. GDPR data-protection controls are therefore N/A here. If this is intentional, record the no-PII posture in an ADR; if the app does process personal data, name those fields conventionally so this dimension activates.
C4 Data Retention — No personal data detected in the analyzed source — no PII-typed entity/column names (Email, FirstName, DateOfBirth, …), no ASP.NET Identity / user-account model, and no stored user credentials. GDPR data-protection controls are therefore N/A here. If this is intentional, record the no-PII posture in an ADR; if the app does process personal data, name those fields conventionally so this dimension activates.
C5 Data-Subject Rights — No personal data detected in the analyzed source — no PII-typed entity/column names (Email, FirstName, DateOfBirth, …), no ASP.NET Identity / user-account model, and no stored user credentials. GDPR data-protection controls are therefore N/A here. If this is intentional, record the no-PII posture in an ADR; if the app does process personal data, name those fields conventionally so this dimension activates.
D10 Test Quality — No tests in the analyzed solution to assess for quality.
D11 Test Reliability — Test reliability not included
D14 License Compliance — license scan produced no result — the tool ran but its JSON output could not be parsed; the offline NuGet fallback resolved nothing
D17 Explicit Debt — the C# workspace loaded 0 projects, so explicit-debt density could not be measured
D18 Solution Shape — Dimension evaluation failed
D19 Documentation Quality — LLM evaluation failed
D22 Internal API Consistency — No exposed public API
D23 Boundary Type-Coupling — Zero projects and zero LoC mean the codebase is trivial and has no structure to justify boundaries.
D24 Comment Value — No inline comments to assess — comment value is not applicable here.
D30 Dependency Vulnerabilities — No .NET solution found; no NuGet dependencies to scan for vulnerabilities.
D32 Data Compliance (PII/GDPR) — No PII/GDPR ruleset is bundled (the public p/gdpr semgrep pack was retired) — data compliance is not assessed in this scan.
D36 Supply-chain Provenance & Signing — The CI pipeline builds and tests but publishes no released artifact — no package publish, container push, GitHub release or deployment step. Supply-chain provenance, signing and SBOM attest RELEASED artifacts, so there is nothing to attest here. Add them to the release pipeline when this repo starts shipping artifacts (a NuGet package, a container image, a GitHub release).
D37 Vulnerability-disclosure Policy — No vulnerability-disclosure policy file found (SECURITY.md, .github/SECURITY.md, docs/SECURITY.md, .well-known/security.txt). A coordinated-disclosure policy may live off-repo, so this is not evidenced rather than failed.
D39 IL Efficiency — The target did not build, so no IL was available to measure.
D6 Cohesion (LCOM4) — No production classes were analyzable, so cohesion (LCOM4) was not measured (the solution likely failed to load or has no production code).
DM1 Domain Modelling — not run — only 1/3 markers (28 value object(s))
ED1 Event-Driven — not run — 0/3 markers found
ED5 Idempotency — no mutating command handlers or message consumers detected — idempotency check not applicable
ES1 Event Sourcing — not run — 0/3 markers found
GD1 Unfinished & placeholder code — no source files
IC1 Incompleteness & stubs — no C# methods found
P12 CI test-gate honesty — no data
P6 Release Hygiene — not evidenced — no changelog, version stamp or semver release tag in the repo
P7 Outbound HTTP resilience — not applicable — this isn't a service/API/worker
P8 Schema migrations — no EF Core usage detected
P9 Domain vs controller coverage — no coverage report found on disk — run tests with `--collect:"XPlat Code Coverage"` (or in CI) to enable this cross-layer check
PF1 Benchmark discipline — Performance is assessed only for perf-relevant repos — a packaged library, one that ships benchmarks, or one already using allocation-aware APIs. This repo isn't one, so the Performance lens is not applicable and is excluded from the score.
PF2 Allocation hygiene — Performance is assessed only for perf-relevant repos — a packaged library, one that ships benchmarks, or one already using allocation-aware APIs. This repo isn't one, so the Performance lens is not applicable and is excluded from the score.
PF3 Async & latency hygiene — Performance is assessed only for perf-relevant repos — a packaged library, one that ships benchmarks, or one already using allocation-aware APIs. This repo isn't one, so the Performance lens is not applicable and is excluded from the score.
R11 Import Boundaries — No recognizable feature-sliced/layered src layout — boundary rules not applicable.
S1 Web-Security Posture — No web surface detected in the analyzed source — no HTTP API or web-UI project (no controllers/minimal-API endpoints, no Razor/Blazor views) and no web middleware (HTTPS redirection, HSTS, security headers, cookies). Transport security, security headers, secure cookies, CSRF/input-validation and middleware-order controls are therefore N/A here — this is a library/CLI/worker, not a web app. Crypto hygiene was still checked and found nothing to flag. If this codebase becomes web-facing, the dimension reactivates automatically.
SC1 Supply-chain hygiene — no data
X2 Cancellation propagation — no async methods found
X5 Nullable reference types — no NRT-eligible projects
Appendix A — Findings (grouped)
The findings behind the scores, grouped by severity, then by dimension and kind. The high-severity issues are enumerated in full below; items per group are capped at 25 with any overflow stated explicitly per group, never silently truncated. The complete machine-readable list of every finding (all severities) is the companion findings.md in this report's bundle.
Issue — 75 finding(s)
D38 · OSV Dependency Vulnerabilities· High CVE · ×38
High CVE: [GHSA redacted] cdk/package-lock.json— aws-cdk-lib 2.243.0: [GHSA redacted] — upgrade to 2.246.0
High CVE: [GHSA redacted] cdk/package-lock.json— aws-cdk-lib 2.243.0: [GHSA redacted] — upgrade to 2.260.0
High CVE: [GHSA redacted] cdk/package-lock.json— brace-expansion 1.1.12: [GHSA redacted] — upgrade to 1.1.16 (in 5 lockfiles)
High CVE: [GHSA redacted] cdk/package-lock.json— brace-expansion 1.1.12: [GHSA redacted] — upgrade to 1.1.17 (in 5 lockfiles)
High CVE: [GHSA redacted] cdk/package-lock.json— brace-expansion 1.1.12: [GHSA redacted] — upgrade to 1.1.18 (in 5 lockfiles)
High CVE: [GHSA redacted] cdk/package-lock.json— fast-uri 3.1.0: [GHSA redacted] — upgrade to 3.1.3
High CVE: [GHSA redacted] cdk/package-lock.json— fast-uri 3.1.0: [GHSA redacted] — upgrade to 3.1.5
High CVE: [GHSA redacted] cdk/package-lock.json— fast-uri 3.1.0: [GHSA redacted] — upgrade to 3.1.1
High CVE: [GHSA redacted] cdk/package-lock.json— fast-uri 3.1.0: [GHSA redacted] — upgrade to 3.1.4
High CVE: [GHSA redacted] cdk/package-lock.json— fast-uri 3.1.0: [GHSA redacted] — upgrade to 3.1.2
High CVE: [GHSA redacted] cdk/package-lock.json— flatted 3.3.3: [GHSA redacted] — upgrade to 3.4.0 (in 2 lockfiles)
High CVE: [GHSA redacted] cdk/package-lock.json— flatted 3.3.3: [GHSA redacted] — upgrade to 3.4.2 (in 2 lockfiles)
High CVE: [GHSA redacted] cdk/package-lock.json— handlebars 4.7.8: [GHSA redacted] — upgrade to 4.7.9
High CVE: [GHSA redacted] cdk/package-lock.json— handlebars 4.7.8: [GHSA redacted] — upgrade to 4.7.9
High CVE: [GHSA redacted] cdk/package-lock.json— handlebars 4.7.8: [GHSA redacted] — upgrade to 4.7.9
High CVE: [GHSA redacted] cdk/package-lock.json— handlebars 4.7.8: [GHSA redacted] — upgrade to 4.7.9
High CVE: [GHSA redacted] cdk/package-lock.json— js-yaml 3.14.2: [GHSA redacted] — upgrade to 3.15.0 (in 5 lockfiles)
High CVE: [GHSA redacted] cdk/package-lock.json— js-yaml 3.14.2: [GHSA redacted] — upgrade to 3.15.1 (in 5 lockfiles)
High CVE: [GHSA redacted] cdk/package-lock.json— minimatch 3.1.2: [GHSA redacted] — upgrade to 3.1.4 (in 4 lockfiles)
High CVE: [GHSA redacted] cdk/package-lock.json— minimatch 3.1.2: [GHSA redacted] — upgrade to 3.1.3 (in 4 lockfiles)
High CVE: [GHSA redacted] cdk/package-lock.json— minimatch 3.1.2: [GHSA redacted] — upgrade to 3.1.3 (in 4 lockfiles)
High CVE: [GHSA redacted] cdk/package-lock.json— picomatch 2.3.1: [GHSA redacted] — upgrade to 2.3.2 (in 4 lockfiles)
High CVE: [GHSA redacted] cdk/package-lock.json— storybook 10.1.4: [GHSA redacted] — upgrade to 10.1.10
High CVE: [GHSA redacted] cdk/package-lock.json— storybook 10.1.4: [GHSA redacted] — upgrade to 10.2.10
High CVE: [GHSA redacted] cdk/package-lock.json— ws 8.18.3: [GHSA redacted] — upgrade to 8.21.0 (in 2 lockfiles)
High: dependabot-missing-cooldown .github/dependabot.yml:5— This Dependabot configuration does not set a cooldown period. Newly published packages can be malicious or unstable. Add a `cooldown` block with `default-days: 7` to each `package-ecosystem` entry under `updates` to wait 7 days before proposing updates to newly published package versions. Reference: https://docs.github.com/en/code-security/dependabot/dependabot-version-updates/configuration-options-for-the-dependabot.yml-file#cooldown
High: dependabot-missing-cooldown .github/dependabot.yml:13— This Dependabot configuration does not set a cooldown period. Newly published packages can be malicious or unstable. Add a `cooldown` block with `default-days: 7` to each `package-ecosystem` entry under `updates` to wait 7 days before proposing updates to newly published package versions. Reference: https://docs.github.com/en/code-security/dependabot/dependabot-version-updates/configuration-options-for-the-dependabot.yml-file#cooldown
High: dependabot-missing-cooldown .github/dependabot.yml:19— This Dependabot configuration does not set a cooldown period. Newly published packages can be malicious or unstable. Add a `cooldown` block with `default-days: 7` to each `package-ecosystem` entry under `updates` to wait 7 days before proposing updates to newly published package versions. Reference: https://docs.github.com/en/code-security/dependabot/dependabot-version-updates/configuration-options-for-the-dependabot.yml-file#cooldown
High: dependabot-missing-cooldown .github/dependabot.yml:25— This Dependabot configuration does not set a cooldown period. Newly published packages can be malicious or unstable. Add a `cooldown` block with `default-days: 7` to each `package-ecosystem` entry under `updates` to wait 7 days before proposing updates to newly published package versions. Reference: https://docs.github.com/en/code-security/dependabot/dependabot-version-updates/configuration-options-for-the-dependabot.yml-file#cooldown
High: dependabot-missing-cooldown .github/dependabot.yml:31— This Dependabot configuration does not set a cooldown period. Newly published packages can be malicious or unstable. Add a `cooldown` block with `default-days: 7` to each `package-ecosystem` entry under `updates` to wait 7 days before proposing updates to newly published package versions. Reference: https://docs.github.com/en/code-security/dependabot/dependabot-version-updates/configuration-options-for-the-dependabot.yml-file#cooldown
High: github-actions-mutable-action-tag .github/workflows/ci.yml:66— GitHub Actions step uses a mutable tag or branch reference. Tags and branch names can be silently repointed by the action owner, enabling supply-chain attacks — as seen in the trivy-action and kics-github-action compromises. Pin the reference to a full 40-character commit SHA instead, e.g. `uses: actions/checkout@8ade135a41bc03ea155e62e844d188df1ea18608`.
High: github-actions-mutable-action-tag .github/workflows/ci.yml:86— GitHub Actions step uses a mutable tag or branch reference. Tags and branch names can be silently repointed by the action owner, enabling supply-chain attacks — as seen in the trivy-action and kics-github-action compromises. Pin the reference to a full 40-character commit SHA instead, e.g. `uses: actions/checkout@8ade135a41bc03ea155e62e844d188df1ea18608`.
High: tainted-sql-string collections/app/controllers/CollectionsController.scala:176— User data flows into this manually-constructed SQL string. User data can be safely inserted into SQL strings using prepared statements or an object-relational mapper (ORM). Manually-constructed SQL strings is a possible indicator of SQL injection, which could let an attacker steal or manipulate data from the database. Instead, use prepared statements (`connection.PreparedStatement`) or a safe library.
High: tainted-sql-string common-lib/src/main/scala/com/gu/mediaservice/lib/aws/DynamoDB.scala:430— User data flows into this manually-constructed SQL string. User data can be safely inserted into SQL strings using prepared statements or an object-relational mapper (ORM). Manually-constructed SQL strings is a possible indicator of SQL injection, which could let an attacker steal or manipulate data from the database. Instead, use prepared statements (`connection.PreparedStatement`) or a safe library.
High: tainted-sql-string cropper/app/controllers/CropperController.scala:119— User data flows into this manually-constructed SQL string. User data can be safely inserted into SQL strings using prepared statements or an object-relational mapper (ORM). Manually-constructed SQL strings is a possible indicator of SQL injection, which could let an attacker steal or manipulate data from the database. Instead, use prepared statements (`connection.PreparedStatement`) or a safe library.
High: tainted-sql-string image-loader/app/controllers/UploadStatusController.scala:27— User data flows into this manually-constructed SQL string. User data can be safely inserted into SQL strings using prepared statements or an object-relational mapper (ORM). Manually-constructed SQL strings is a possible indicator of SQL injection, which could let an attacker steal or manipulate data from the database. Instead, use prepared statements (`connection.PreparedStatement`) or a safe library.
High: tainted-sql-string image-loader/app/controllers/UploadStatusController.scala:47— User data flows into this manually-constructed SQL string. User data can be safely inserted into SQL strings using prepared statements or an object-relational mapper (ORM). Manually-constructed SQL strings is a possible indicator of SQL injection, which could let an attacker steal or manipulate data from the database. Instead, use prepared statements (`connection.PreparedStatement`) or a safe library.
High: tainted-sql-string media-api/app/lib/ImageResponse.scala:167— User data flows into this manually-constructed SQL string. User data can be safely inserted into SQL strings using prepared statements or an object-relational mapper (ORM). Manually-constructed SQL strings is a possible indicator of SQL injection, which could let an attacker steal or manipulate data from the database. Instead, use prepared statements (`connection.PreparedStatement`) or a safe library.
High: tainted-sql-string media-api/app/lib/ImageResponse.scala:168— User data flows into this manually-constructed SQL string. User data can be safely inserted into SQL strings using prepared statements or an object-relational mapper (ORM). Manually-constructed SQL strings is a possible indicator of SQL injection, which could let an attacker steal or manipulate data from the database. Instead, use prepared statements (`connection.PreparedStatement`) or a safe library.
High: tainted-sql-string media-api/app/lib/ImageResponse.scala:169— User data flows into this manually-constructed SQL string. User data can be safely inserted into SQL strings using prepared statements or an object-relational mapper (ORM). Manually-constructed SQL strings is a possible indicator of SQL injection, which could let an attacker steal or manipulate data from the database. Instead, use prepared statements (`connection.PreparedStatement`) or a safe library.
High: tainted-sql-string media-api/app/lib/ImageResponse.scala:171— User data flows into this manually-constructed SQL string. User data can be safely inserted into SQL strings using prepared statements or an object-relational mapper (ORM). Manually-constructed SQL strings is a possible indicator of SQL injection, which could let an attacker steal or manipulate data from the database. Instead, use prepared statements (`connection.PreparedStatement`) or a safe library.
High: tainted-sql-string media-api/app/lib/ImageResponse.scala:172— User data flows into this manually-constructed SQL string. User data can be safely inserted into SQL strings using prepared statements or an object-relational mapper (ORM). Manually-constructed SQL strings is a possible indicator of SQL injection, which could let an attacker steal or manipulate data from the database. Instead, use prepared statements (`connection.PreparedStatement`) or a safe library.
High: tainted-sql-string media-api/app/lib/ImageResponse.scala:173— User data flows into this manually-constructed SQL string. User data can be safely inserted into SQL strings using prepared statements or an object-relational mapper (ORM). Manually-constructed SQL strings is a possible indicator of SQL injection, which could let an attacker steal or manipulate data from the database. Instead, use prepared statements (`connection.PreparedStatement`) or a safe library.
High: tainted-sql-string media-api/app/lib/ImageResponse.scala:174— User data flows into this manually-constructed SQL string. User data can be safely inserted into SQL strings using prepared statements or an object-relational mapper (ORM). Manually-constructed SQL strings is a possible indicator of SQL injection, which could let an attacker steal or manipulate data from the database. Instead, use prepared statements (`connection.PreparedStatement`) or a safe library.
High: tainted-sql-string media-api/app/lib/ImageResponse.scala:364— User data flows into this manually-constructed SQL string. User data can be safely inserted into SQL strings using prepared statements or an object-relational mapper (ORM). Manually-constructed SQL strings is a possible indicator of SQL injection, which could let an attacker steal or manipulate data from the database. Instead, use prepared statements (`connection.PreparedStatement`) or a safe library.
High: tainted-sql-string media-api/app/lib/ImageResponse.scala:382— User data flows into this manually-constructed SQL string. User data can be safely inserted into SQL strings using prepared statements or an object-relational mapper (ORM). Manually-constructed SQL strings is a possible indicator of SQL injection, which could let an attacker steal or manipulate data from the database. Instead, use prepared statements (`connection.PreparedStatement`) or a safe library.
High: tainted-sql-string thrall/app/lib/elasticsearch/ElasticSearch.scala:309— User data flows into this manually-constructed SQL string. User data can be safely inserted into SQL strings using prepared statements or an object-relational mapper (ORM). Manually-constructed SQL strings is a possible indicator of SQL injection, which could let an attacker steal or manipulate data from the database. Instead, use prepared statements (`connection.PreparedStatement`) or a safe library.
High: tainted-sql-string thrall/app/lib/elasticsearch/ElasticSearch.scala:324— User data flows into this manually-constructed SQL string. User data can be safely inserted into SQL strings using prepared statements or an object-relational mapper (ORM). Manually-constructed SQL strings is a possible indicator of SQL injection, which could let an attacker steal or manipulate data from the database. Instead, use prepared statements (`connection.PreparedStatement`) or a safe library.
High: tainted-sql-string thrall/app/lib/elasticsearch/ElasticSearch.scala:367— User data flows into this manually-constructed SQL string. User data can be safely inserted into SQL strings using prepared statements or an object-relational mapper (ORM). Manually-constructed SQL strings is a possible indicator of SQL injection, which could let an attacker steal or manipulate data from the database. Instead, use prepared statements (`connection.PreparedStatement`) or a safe library.
High: tainted-sql-string thrall/app/lib/elasticsearch/ElasticSearch.scala:402— User data flows into this manually-constructed SQL string. User data can be safely inserted into SQL strings using prepared statements or an object-relational mapper (ORM). Manually-constructed SQL strings is a possible indicator of SQL injection, which could let an attacker steal or manipulate data from the database. Instead, use prepared statements (`connection.PreparedStatement`) or a safe library.
Boundary-crossing change coupling: image-embedder-lambda.ts ↔ embedder.ts cdk/lib/image-embedder-lambda.ts— `cdk/lib/image-embedder-lambda.ts` (context cdk) and `image-embedder-lambda/src/embedder/embedder.ts` (context image-embedder-lambda) live in DIFFERENT modules yet change together 52% of the time (12 shared commits) — the bounded-context boundary may be in the wrong place, or one context is leaking into the other. This is the behavioural boundary violation a static scan can't see.
Boundary-crossing change coupling: image-embedder-lambda.ts ↔ imageResolver.ts cdk/lib/image-embedder-lambda.ts— `cdk/lib/image-embedder-lambda.ts` (context cdk) and `image-embedder-lambda/src/embedder/imageResolver.ts` (context image-embedder-lambda) live in DIFFERENT modules yet change together 50% of the time (8 shared commits) — the bounded-context boundary may be in the wrong place, or one context is leaking into the other. This is the behavioural boundary violation a static scan can't see.
Boundary-crossing change coupling: image-embedder-lambda.ts ↔ imageEmbedder.ts cdk/lib/image-embedder-lambda.ts— `cdk/lib/image-embedder-lambda.ts` (context cdk) and `image-embedder-lambda/src/embedder/imageEmbedder.ts` (context image-embedder-lambda) live in DIFFERENT modules yet change together 50% of the time (7 shared commits) — the bounded-context boundary may be in the wrong place, or one context is leaking into the other. This is the behavioural boundary violation a static scan can't see.
Medium: var-in-href kahuna/public/js/common/user-actions.html:12— Detected a template variable used in an anchor tag with the 'href' attribute. This allows a malicious actor to input the 'javascript:' URI and is subject to cross- site scripting (XSS) attacks. If using Flask, use 'url_for()' to safely generate a URL. If using Django, use the 'url' filter to safely generate a URL. If using Mustache, use a URL encoding library, or prepend a slash '/' to the variable for relative links (`href="/{{link}}"`). You may also consider setting the Content Security Policy (CSP) header.
Medium: var-in-href kahuna/public/js/common/user-actions.html:18— Detected a template variable used in an anchor tag with the 'href' attribute. This allows a malicious actor to input the 'javascript:' URI and is subject to cross- site scripting (XSS) attacks. If using Flask, use 'url_for()' to safely generate a URL. If using Django, use the 'url' filter to safely generate a URL. If using Mustache, use a URL encoding library, or prepend a slash '/' to the variable for relative links (`href="/{{link}}"`). You may also consider setting the Content Security Policy (CSP) header.
Medium: var-in-href kahuna/public/js/common/user-actions.html:40— Detected a template variable used in an anchor tag with the 'href' attribute. This allows a malicious actor to input the 'javascript:' URI and is subject to cross- site scripting (XSS) attacks. If using Flask, use 'url_for()' to safely generate a URL. If using Django, use the 'url' filter to safely generate a URL. If using Mustache, use a URL encoding library, or prepend a slash '/' to the variable for relative links (`href="/{{link}}"`). You may also consider setting the Content Security Policy (CSP) header.
Medium: var-in-href kahuna/public/js/components/gr-display-crops/gr-display-crops.html:22— Detected a template variable used in an anchor tag with the 'href' attribute. This allows a malicious actor to input the 'javascript:' URI and is subject to cross- site scripting (XSS) attacks. If using Flask, use 'url_for()' to safely generate a URL. If using Django, use the 'url' filter to safely generate a URL. If using Mustache, use a URL encoding library, or prepend a slash '/' to the variable for relative links (`href="/{{link}}"`). You may also consider setting the Content Security Policy (CSP) header.
Medium: var-in-href kahuna/public/js/components/gr-downloader/gr-downloader.html:26— Detected a template variable used in an anchor tag with the 'href' attribute. This allows a malicious actor to input the 'javascript:' URI and is subject to cross- site scripting (XSS) attacks. If using Flask, use 'url_for()' to safely generate a URL. If using Django, use the 'url' filter to safely generate a URL. If using Mustache, use a URL encoding library, or prepend a slash '/' to the variable for relative links (`href="/{{link}}"`). You may also consider setting the Content Security Policy (CSP) header.
Medium: var-in-href kahuna/public/js/components/gr-downloader/gr-downloader.html:41— Detected a template variable used in an anchor tag with the 'href' attribute. This allows a malicious actor to input the 'javascript:' URI and is subject to cross- site scripting (XSS) attacks. If using Flask, use 'url_for()' to safely generate a URL. If using Django, use the 'url' filter to safely generate a URL. If using Mustache, use a URL encoding library, or prepend a slash '/' to the variable for relative links (`href="/{{link}}"`). You may also consider setting the Content Security Policy (CSP) header.
Medium: var-in-href kahuna/public/js/components/gr-image-usage/gr-image-usage-list.html:16— Detected a template variable used in an anchor tag with the 'href' attribute. This allows a malicious actor to input the 'javascript:' URI and is subject to cross- site scripting (XSS) attacks. If using Flask, use 'url_for()' to safely generate a URL. If using Django, use the 'url' filter to safely generate a URL. If using Mustache, use a URL encoding library, or prepend a slash '/' to the variable for relative links (`href="/{{link}}"`). You may also consider setting the Content Security Policy (CSP) header.
Medium: var-in-href kahuna/public/js/components/gr-image-usage/gr-image-usage-list.html:25— Detected a template variable used in an anchor tag with the 'href' attribute. This allows a malicious actor to input the 'javascript:' URI and is subject to cross- site scripting (XSS) attacks. If using Flask, use 'url_for()' to safely generate a URL. If using Django, use the 'url' filter to safely generate a URL. If using Mustache, use a URL encoding library, or prepend a slash '/' to the variable for relative links (`href="/{{link}}"`). You may also consider setting the Content Security Policy (CSP) header.
Medium: var-in-href kahuna/public/js/errors/global.html:5— Detected a template variable used in an anchor tag with the 'href' attribute. This allows a malicious actor to input the 'javascript:' URI and is subject to cross- site scripting (XSS) attacks. If using Flask, use 'url_for()' to safely generate a URL. If using Django, use the 'url' filter to safely generate a URL. If using Mustache, use a URL encoding library, or prepend a slash '/' to the variable for relative links (`href="/{{link}}"`). You may also consider setting the Content Security Policy (CSP) header.
Medium: var-in-href kahuna/public/js/errors/global.html:9— Detected a template variable used in an anchor tag with the 'href' attribute. This allows a malicious actor to input the 'javascript:' URI and is subject to cross- site scripting (XSS) attacks. If using Flask, use 'url_for()' to safely generate a URL. If using Django, use the 'url' filter to safely generate a URL. If using Mustache, use a URL encoding library, or prepend a slash '/' to the variable for relative links (`href="/{{link}}"`). You may also consider setting the Content Security Policy (CSP) header.
Medium: var-in-href kahuna/public/js/errors/global.html:19— Detected a template variable used in an anchor tag with the 'href' attribute. This allows a malicious actor to input the 'javascript:' URI and is subject to cross- site scripting (XSS) attacks. If using Flask, use 'url_for()' to safely generate a URL. If using Django, use the 'url' filter to safely generate a URL. If using Mustache, use a URL encoding library, or prepend a slash '/' to the variable for relative links (`href="/{{link}}"`). You may also consider setting the Content Security Policy (CSP) header.
Medium: var-in-href kahuna/public/js/errors/global.html:25— Detected a template variable used in an anchor tag with the 'href' attribute. This allows a malicious actor to input the 'javascript:' URI and is subject to cross- site scripting (XSS) attacks. If using Flask, use 'url_for()' to safely generate a URL. If using Django, use the 'url' filter to safely generate a URL. If using Mustache, use a URL encoding library, or prepend a slash '/' to the variable for relative links (`href="/{{link}}"`). You may also consider setting the Content Security Policy (CSP) header.
Medium: var-in-href kahuna/public/js/errors/global.html:32— Detected a template variable used in an anchor tag with the 'href' attribute. This allows a malicious actor to input the 'javascript:' URI and is subject to cross- site scripting (XSS) attacks. If using Flask, use 'url_for()' to safely generate a URL. If using Django, use the 'url' filter to safely generate a URL. If using Mustache, use a URL encoding library, or prepend a slash '/' to the variable for relative links (`href="/{{link}}"`). You may also consider setting the Content Security Policy (CSP) header.
Medium: var-in-href kahuna/public/js/errors/global.html:38— Detected a template variable used in an anchor tag with the 'href' attribute. This allows a malicious actor to input the 'javascript:' URI and is subject to cross- site scripting (XSS) attacks. If using Flask, use 'url_for()' to safely generate a URL. If using Django, use the 'url' filter to safely generate a URL. If using Mustache, use a URL encoding library, or prepend a slash '/' to the variable for relative links (`href="/{{link}}"`). You may also consider setting the Content Security Policy (CSP) header.
Medium: var-in-href kahuna/public/js/errors/global.html:49— Detected a template variable used in an anchor tag with the 'href' attribute. This allows a malicious actor to input the 'javascript:' URI and is subject to cross- site scripting (XSS) attacks. If using Flask, use 'url_for()' to safely generate a URL. If using Django, use the 'url' filter to safely generate a URL. If using Mustache, use a URL encoding library, or prepend a slash '/' to the variable for relative links (`href="/{{link}}"`). You may also consider setting the Content Security Policy (CSP) header.
Medium: var-in-href kahuna/public/js/errors/global.html:59— Detected a template variable used in an anchor tag with the 'href' attribute. This allows a malicious actor to input the 'javascript:' URI and is subject to cross- site scripting (XSS) attacks. If using Flask, use 'url_for()' to safely generate a URL. If using Django, use the 'url' filter to safely generate a URL. If using Mustache, use a URL encoding library, or prepend a slash '/' to the variable for relative links (`href="/{{link}}"`). You may also consider setting the Content Security Policy (CSP) header.
Medium: var-in-href kahuna/public/js/image/view.html:178— Detected a template variable used in an anchor tag with the 'href' attribute. This allows a malicious actor to input the 'javascript:' URI and is subject to cross- site scripting (XSS) attacks. If using Flask, use 'url_for()' to safely generate a URL. If using Django, use the 'url' filter to safely generate a URL. If using Mustache, use a URL encoding library, or prepend a slash '/' to the variable for relative links (`href="/{{link}}"`). You may also consider setting the Content Security Policy (CSP) header.
Medium: wildcard-postmessage-configuration kahuna/public/js/main.js:268— The target origin of the window.postMessage() API is set to "*". This could allow for information disclosure due to the possibility of any origin allowed to receive the message.
Medium: var-in-href kahuna/public/js/upload/view.html:29— Detected a template variable used in an anchor tag with the 'href' attribute. This allows a malicious actor to input the 'javascript:' URI and is subject to cross- site scripting (XSS) attacks. If using Flask, use 'url_for()' to safely generate a URL. If using Django, use the 'url' filter to safely generate a URL. If using Mustache, use a URL encoding library, or prepend a slash '/' to the variable for relative links (`href="/{{link}}"`). You may also consider setting the Content Security Policy (CSP) header.
Medium: var-in-href kahuna/public/js/usage-rights/usage-rights-editor.html:9— Detected a template variable used in an anchor tag with the 'href' attribute. This allows a malicious actor to input the 'javascript:' URI and is subject to cross- site scripting (XSS) attacks. If using Flask, use 'url_for()' to safely generate a URL. If using Django, use the 'url' filter to safely generate a URL. If using Mustache, use a URL encoding library, or prepend a slash '/' to the variable for relative links (`href="/{{link}}"`). You may also consider setting the Content Security Policy (CSP) header.
D38 · OSV Dependency Vulnerabilities· Medium CVE · ×10
Medium CVE: [GHSA redacted] cdk/package-lock.json— ajv 6.12.6: [GHSA redacted] — upgrade to 6.14.0 (in 2 lockfiles)
Medium CVE: [GHSA redacted] cdk/package-lock.json— brace-expansion 1.1.12: [GHSA redacted] — upgrade to 1.1.13 (in 5 lockfiles)
Medium CVE: [GHSA redacted] cdk/package-lock.json— brace-expansion 5.0.3: [GHSA redacted] — upgrade to 5.0.6
Medium CVE: [GHSA redacted] cdk/package-lock.json— fast-xml-parser 5.5.8: [GHSA redacted] — upgrade to 5.7.0 (in 2 lockfiles)
Medium CVE: [GHSA redacted] cdk/package-lock.json— handlebars 4.7.8: [GHSA redacted] — upgrade to 4.7.9
Medium CVE: [GHSA redacted] cdk/package-lock.json— handlebars 4.7.8: [GHSA redacted] — upgrade to 4.7.9
Medium CVE: [GHSA redacted] cdk/package-lock.json— js-yaml 3.14.2: [GHSA redacted] — upgrade to 3.15.0 (in 5 lockfiles)
Medium CVE: [GHSA redacted] cdk/package-lock.json— picomatch 2.3.1: [GHSA redacted] — upgrade to 2.3.2 (in 4 lockfiles)
Medium CVE: [GHSA redacted] cdk/package-lock.json— ws 8.18.3: [GHSA redacted] — upgrade to 8.20.1 (in 2 lockfiles)
Medium CVE: [GHSA redacted] cdk/package-lock.json— yaml 1.10.2: [GHSA redacted] — upgrade to 1.10.3
Change coupling: gr-chips.js ↔ gr-text-chip.js kahuna/public/js/components/gr-chips/gr-chips.js— `kahuna/public/js/components/gr-chips/gr-chips.js` and `kahuna/public/js/components/gr-chips/gr-text-chip.js` change together 87% of the time (13 shared commits) with no explicit dependency — a hidden/logical coupling. If they belong together, co-locate them; if not, break the coupling.
Change coupling: imageResolver.ts ↔ models.ts image-embedder-lambda/src/embedder/imageResolver.ts— `image-embedder-lambda/src/embedder/imageResolver.ts` and `image-embedder-lambda/src/embedder/models.ts` change together 82% of the time (9 shared commits) with no explicit dependency — a hidden/logical coupling. If they belong together, co-locate them; if not, break the coupling.
Change coupling: backfiller.ts ↔ localRun.ts image-embedder-lambda/src/backfiller/backfiller.ts— `image-embedder-lambda/src/backfiller/backfiller.ts` and `image-embedder-lambda/src/backfiller/localRun.ts` change together 82% of the time (9 shared commits) with no explicit dependency — a hidden/logical coupling. If they belong together, co-locate them; if not, break the coupling.
Change coupling: edits-api.js ↔ media-api.js kahuna/public/js/services/api/edits-api.js— `kahuna/public/js/services/api/edits-api.js` and `kahuna/public/js/services/api/media-api.js` change together 80% of the time (8 shared commits) with no explicit dependency — a hidden/logical coupling. If they belong together, co-locate them; if not, break the coupling.
Change coupling: webpack.config.dev.js ↔ webpack.config.prod.js kahuna/webpack.config.dev.js— `kahuna/webpack.config.dev.js` and `kahuna/webpack.config.prod.js` change together 80% of the time (8 shared commits) with no explicit dependency — a hidden/logical coupling. If they belong together, co-locate them; if not, break the coupling.
Change coupling: embedder.ts ↔ imageEmbedder.ts image-embedder-lambda/src/embedder/embedder.ts— `image-embedder-lambda/src/embedder/embedder.ts` and `image-embedder-lambda/src/embedder/imageEmbedder.ts` change together 79% of the time (11 shared commits) with no explicit dependency — a hidden/logical coupling. If they belong together, co-locate them; if not, break the coupling.
Change coupling: gr-display-crops.js ↔ controller.js kahuna/public/js/components/gr-display-crops/gr-display-crops.js— `kahuna/public/js/components/gr-display-crops/gr-display-crops.js` and `kahuna/public/js/image/controller.js` change together 77% of the time (10 shared commits) with no explicit dependency — a hidden/logical coupling. If they belong together, co-locate them; if not, break the coupling.
Hotspot: kahuna/public/js/search/query.js kahuna/public/js/search/query.js— kahuna/public/js/search/query.js changed 16 times in last 90 days, max complexity 22. 1 of those changes were fix/bug commits — a defect-dense hotspot worth prioritising.
Hotspot: kahuna/public/js/search/structured-query/syntax.ts kahuna/public/js/search/structured-query/syntax.ts— kahuna/public/js/search/structured-query/syntax.ts changed 7 times in last 90 days, max complexity 15.
LLM evaluation failed — JSON parse error: Expected end of string, but instead reached end of data. Path: $.findings[0].docPath | LineNumber: 0 | BytePositionInLine: 1183.
D20 · ADR Quality· No context/problem and no decision; only a file-structure reference · ×1
No context/problem and no decision; only a file-structure reference docs/00-about/02-structure.md— Replace with the real problem (e.g. inconsistent cross-project documentation) and the explicit decision (e.g. centralise architecture docs in README.md) plus consequences
D20 · ADR Quality· Decision is an install list with no context/problem and no consequences/trade-offs · ×1
Decision is an install list with no context/problem and no consequences/trade-offs docs/01-setup/01-software-dependencies.md— Add a Context section explaining why these tools are required (build/CI/continuous delivery) and a Consequences section on alternatives or trade-offs
D20 · ADR Quality· No context/problem and no consequences/trade-offs; only a one-line dependency list with credentials and resource linkage is present · ×1
No context/problem and no consequences/trade-offs; only a one-line dependency list with credentials and resource linkage is present docs/01-setup/02-aws-dependencies.md— Add why AWS dependencies are needed (e.g. S3 for data storage, DynamoDB for structured data) and the trade-offs of using localstack vs real AWS in production
D20 · ADR Quality· No consequences/trade-offs section (the host-resolve-to-127.0.0.1 requirement and the need to add each subdomain via hosts are noted but not framed as trade-offs) · ×1
No consequences/trade-offs section (the host-resolve-to-127.0.0.1 requirement and the need to add each subdomain via hosts are noted but not framed as trade-offs) docs/01-setup/03-configuring-setup.md— Add a Consequences section noting that changing DOMAIN breaks access on media.local.dev-gutools.co.uk until the hosts entry is added, and that dev-nginx must be used for any new subdomains
D20 · ADR Quality· The body is a runnable setup script with flags but the context/problem driving the decision and any consequences/trade-offs are absent · ×1
The body is a runnable setup script with flags but the context/problem driving the decision and any consequences/trade-offs are absent docs/01-setup/03-running-setup.md— Add a Context section explaining why local development of Grid requires this setup (e.g. Docker/CloudFormation/localstack/nginx auth stack) and a Consequences section noting trade-offs such as the need to run setup.sh each time, the authentication flag's real-world cost, and how to handle failures
D20 · ADR Quality· No context/problem and no consequences/trade-offs; only a runnable script list with no rationale for why local running matters · ×1
No context/problem and no consequences/trade-offs; only a runnable script list with no rationale for why local running matters docs/02-running/01-running-locally.md— Add the problem (e.g. CI/CD needs to run locally without external auth) and the trade-offs of each flag (e.g. test environment vs real data, long setup time)
D20 · ADR Quality· No context/problem and no decision; only a name and build command are present · ×1
No context/problem and no decision; only a name and build command are present docs/02-running/02-kahuna.md— Add the problem Kahuna solves (e.g. single-page app with hot-reload) and the explicit architectural decision it embodies
D20 · ADR Quality· Consequences/trade-offs of adding a Stopwatch logging construct are not stated in the visible portion (e.g. overhead of wrapping every do_thing) · ×1
Consequences/trade-offs of adding a Stopwatch logging construct are not stated in the visible portion (e.g. overhead of wrapping every do_thing) docs/02-running/03-logging.md— Add a Consequences section noting that each function call now adds a log line and any performance cost of the Stopwatch wrapper
D20 · ADR Quality· No context/problem and no consequences/trade-offs; only the decision (API-only root collections with a script) is stated · ×1
No context/problem and no consequences/trade-offs; only the decision (API-only root collections with a script) is stated docs/03-apis/02-collections.md— Add why root collections need a UI versus being created via API, and note trade-offs such as requiring an external dev script and the risk of human error
D20 · ADR Quality· Only a one-line failure pattern is stated; no context/problem (what dependency was missing) and no consequences/trade-offs · ×1
Only a one-line failure pattern is stated; no context/problem (what dependency was missing) and no consequences/trade-offs docs/04-troubleshooting/02-sbt.md— State the specific dependency that failed to resolve and its impact on build, plus add a Consequences section noting retry cost and cache persistence
D20 · ADR Quality· No consequences/trade-offs section (e.g. certificate rotation cost, dependency on Java_HOME) · ×1
No consequences/trade-offs section (e.g. certificate rotation cost, dependency on Java_HOME) docs/04-troubleshooting/04-ssl-certs.md— Add a Consequences section noting that mkcert installation and certificate replacement require JAVA_HOME to be exported, and the cost of re-issuing/replacing the root cert when needed
D20 · ADR Quality· No consequences/trade-offs section describing why layered TIFFs are handled this way and what could go wrong · ×1
No consequences/trade-offs section describing why layered TIFFs are handled this way and what could go wrong docs/06-objects-of-interest/01-tiffs.md— Add a Consequences section noting that handling each layer separately (moving -n files) can be error-prone for new users or when the original file is renamed/modified, and how it avoids exploding the output directory
D20 · ADR Quality· No context/problem and no consequences/trade-offs; only a one-line status line and the template link · ×1
No context/problem and no consequences/trade-offs; only a one-line status line and the template link docs/99-archives/01-cloudformation.md— Add why this setup is needed (e.g. running Grid without a shared stack) and what happens if it fails or is broken
D20 · ADR Quality· Uninformative title and the body is a runnable setup guide that reads like an installation list rather than a documented decision (e.g. what trade-offs or consequences are present for each step) · ×1
Uninformative title and the body is a runnable setup guide that reads like an installation list rather than a documented decision (e.g. what trade-offs or consequences are present for each step) docs/99-archives/02.01-dev-setup.md— Give the ADR a descriptive title summarizing its content (e.g. 'Local Development Environment Requirements and Configuration') so readers can find it without reading the body.
D20 · ADR Quality· No context/problem and no consequences; only a one-line file-location statement plus a link to generate-config · ×1
No context/problem and no consequences; only a one-line file-location statement plus a link to generate-config docs/99-archives/02.02-configuration.md— Add why configuration is stored in /etc/grid vs ~/.grid (e.g. secrets/production vs dev flexibility) and the trade-offs of each path
D20 · ADR Quality· The title 'Grid API' is a bare label with no context; the body is a list of links pointing to documentation rather than describing what the Grid API does and its consequences · ×1
The title 'Grid API' is a bare label with no context; the body is a list of links pointing to documentation rather than describing what the Grid API does and its consequences docs/99-archives/04.01-api.md— Give the ADR a descriptive title (e.g. "Direct media API access for kahuna") explaining the problem, then state the decision (how to hit the media API directly via curl) with authentication/authorization requirements and consequences/trade-offs
D20 · ADR Quality· Context/problem is thin; only authentication options and one short test example are stated (the key-creation command is shown but its purpose is not explained) · ×1
Context/problem is thin; only authentication options and one short test example are stated (the key-creation command is shown but its purpose is not explained) docs/99-archives/04.02-authentication.md— Add a Context section explaining when each auth type applies (client-server vs server-server) and why keys are needed over Panda Auth, then add a Consequences section on trade-offs such as the 10-minute sync window and the need to delete keys locally
D20 · ADR Quality· No context/problem and no consequences; the body is a one-line curl command with no rationale or trade-offs · ×1
No context/problem and no consequences; the body is a one-line curl command with no rationale or trade-offs docs/99-archives/04.03-upload-image.md— Add why direct image upload matters (e.g. faster uploads than multipart), the problem it solves, and its alternatives, then document the consequences such as key exposure risk from TARGET_KEY in the curl
D20 · ADR Quality· No context/problem and no consequences; only the one-line decision "Thrall needs to be manually started before it will ingest images." plus a bare curl command · ×1
No context/problem and no consequences; only the one-line decision "Thrall needs to be manually started before it will ingest images." plus a bare curl command docs/99-archives/04.04-start-thrall.md— Add why Thrall requires manual start (e.g. it is an API endpoint that cannot be triggered from outside) and the consequences of this design, such as how to restart it after failures or what happens if someone forgets
D20 · ADR Quality· The body is a single curl example with no context/problem and no explicit decision; the only content is md5sum output · ×1
The body is a single curl example with no context/problem and no explicit decision; the only content is md5sum output docs/99-archives/04.05-media-api.md— Replace the code block with an explanatory problem (e.g. why media requests require authentication) plus the actual API decision (e.g. how to obtain and use the target key, what happens if it fails)
D20 · ADR Quality· No context/problem and no consequences/trade-offs; only a one-line decision with runnable steps and an arbitrary URL (logs.local.dev-gutools.co.uk) without any rationale · ×1
No context/problem and no consequences/trade-offs; only a one-line decision with runnable steps and an arbitrary URL (logs.local.dev-gutools.co.uk) without any rationale docs/99-archives/05-logging.md— Add the problem being solved (e.g. centralized log aggregation needed for debugging or compliance), the trade-offs of running ELK locally (maintenance, storage cost), and why the Stopwatch wrapper is preferable over a custom logging layer
Low: request-host-used common-lib/src/main/scala/com/gu/mediaservice/lib/config/Services.scala:90— '$http_host' and '$host' variables may contain a malicious value from attacker controlled 'Host' request header. Use an explicitly configured host value or a allow list for validation.
Low: header-redefinition dev/imgops/nginx.conf:38— The 'add_header' directive is called in a 'location' block after headers have been set at the server block. Calling 'add_header' in the location block will actually overwrite the headers defined in the server block, no matter which headers are set. To fix this, explicitly set all headers or set all headers in the server block.
D11 · Test Reliability· Test reliability not included · ×1
Test reliability not included — No test projects found, so reliability couldn't be assessed.
D28 · Secrets (history)· Rotate the exposed credentials · ×1
Rotate the exposed credentials — git history can't be un-committed — Some of these secrets are in git HISTORY: deleting the file does not remove them (the commit persists on every clone, fork and backup). The remediation is to ROTATE each historically-exposed credential and treat it as compromised — not to delete the file. Rewriting history is disruptive and unreliable across existing forks. (Working-tree-only secrets — no commit — can instead be removed from the file and moved to a secret store.)
No tests found — No test projects found in the repository.
Info — 1 finding(s)
D22 · Internal API Consistency· No exposed public API · ×1
No exposed public API — No intentionally-exposed types (IsPackable or .Contracts) to evaluate.
Appendix B — Reproduction & audit trail
Every external tool invocation behind a deep-scan dimension — the tool, its captured version, the exact command, how many findings it yielded, and a link to the retained raw output. To reproduce any finding: check out the same commit and run the command shown (repo-relative — never an absolute scratch path). The complete raw scanner output is retained verbatim under artifacts/raw/ (indexed in artifacts/raw/index.json); per-invocation exit codes and wall-clock durations are in sidecar.json — kept out of this table so the rendered report stays byte-identical across runs of the same commit.
provenance: not applicable — The CI pipeline builds and tests but publishes no released artifact — no package publish, container push, GitHub release or deployment step. Supply-chain provenance, signing and SBOM attest RELEASED artifacts, so there is nothing to attest here. Add them to the release pipeline when this repo starts shipping artifacts (a NuGet package, a container image, a GitHub release).
disclosure: not applicable — No vulnerability-disclosure policy file found (SECURITY.md, .github/SECURITY.md, docs/SECURITY.md, .well-known/security.txt). A coordinated-disclosure policy may live off-repo, so this is not evidenced rather than failed.
Run 019fdd15-d944-7dc5-9968-df35d2622f1b · every finding is also locatable in findings.md, and the complete scoring record (with exit codes + durations) in sidecar.json.
Issues: 75 · Warnings: 91 · Recommendations: 6 · Info: 1 — Appendix A · all findings · full markdown report.
Generated by Watchdog — deterministic code-health analysis. 07-08-2026 @ 16:37 UTC.
Downloadable artifacts
Machine-readable and reproducible from this commit + frozen rubric — drop them straight into a contract appendix, a CRA dossier, or a downstream SCA / VEX tool.