google-research

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https://github.com/google-research/google-research.git · scanned 2026-05-16 13:30 UTC (1 day, 6 hours ago) · 10 languages

57 findings 8/10 scanners ran 54th percentile · Python · medium (20-100K LoC)

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UNIFIED Repobility · multi-layer engine · AI coders

Complete repo analysis

51 findings from 1 source. Findings combine the legacy security pipeline AND the multi-layer engine (atlas, wiring, flows, ranked) AND verified AI agent contributions.

{# ── 2026-05-17 R27 #5: score breakdown panel ────────────────────── Surfaces the score_breakdown JSON that's been silently stored on Repository for months. Turns hidden math into a trust signal. #}

Score breakdown â 2026-05-17-v4 calibration-aware

Component	Sub-score	Weight	Contribution
`structure_score`	40.0	0.15	6.00
`security_score`	55.5	0.25	13.88
`testing_score`	13.0	0.20	2.60
`documentation_score`	65.0	0.15	9.75
`practices_score`	30.0	0.15	4.50
`code_quality`	80.0	0.10	8.00
Overall		1.00	44.7

Calibrated penalty buckets (security_score): threat: 44.6

security_score may be inflated — optional scanners skipped due to repo size/fast scan

Severity distribution — click a segment to filter

Active filters: excluding tests × Reset all

Bug-class explainers. Each card groups findings of the same shape — these are the patterns most likely to ship to prod and reappear in future scans unless you systematically fix the cause, not just the instance.

Fragile runtime 32 findings

What it is: Code that runs but breaks under predictable input — division by zero, missing keys, unbounded loops, off-by-one slicing.

Why it matters: Reaches production undetected because happy-path tests pass. First user with a weird input crashes the request.

How AI causes it: AI loves writing the happy path; doesn't probe edge cases unless explicitly asked.

Fix approach: Add property-based tests. Wrap external inputs with explicit validators. Use the framework's typed deserializer (Pydantic, attrs).

12 matching findings on this repo

medium Parallel implementation file sits beside a canonical file CardBench_zero_shot_cardinality_training/generate…:1
low Duplicate top-level symbol appears in a patch-style file CardBench_zero_shot_cardinality_training/generate…:1
low Duplicated implementation block across source files CardBench_zero_shot_cardinality_training/generate…:7
low Duplicated implementation block across source files CardBench_zero_shot_cardinality_training/calculat…:3
low Duplicated implementation block across source files CardBench_zero_shot_cardinality_training/calculat…:2
low Duplicated implementation block across source files CardBench_zero_shot_cardinality_training/calculat…:181
low Duplicated implementation block across source files COSTAR/src/models/utils_transformer.py:25
low Duplicated implementation block across source files COSTAR/src/models/rmsn.py:199
low Duplicated implementation block across source files COSTAR/src/models/rep_est/rep_est.py:233
low Duplicated implementation block across source files COSTAR/src/models/rep_est/rep_est.py:84
low Duplicated implementation block across source files COSTAR/src/models/rep_est/moco.py:46
low Duplicated implementation block across source files COSTAR/src/models/rep_est/ct.py:15

View all fragile runtime findings →

Config drift 1 finding

What it is: Settings duplicated across env files, Docker compose, K8s, and code defaults, all with slightly different values.

Why it matters: Production behaviour depends on whichever copy your loader reads first. Subtle bugs in staging that don't reproduce in dev.

How AI causes it: AI writes new config from memory rather than reading the existing source.

Fix approach: Pick one source of truth (env vars + a settings module). Have every other place import from there. Lint for duplicates in CI.

1 matching finding on this repo

medium No CI/CD configuration found

View all config drift findings →

{# ── 2026-05-17 Round 14: AI-agent bridge footer ────────────────────── Discoverability: the /agents/voting/ guide + MCP manifest exist but aren't linked from anywhere users actually land. Small, opt-in footer. #}

For AI agents: Voting guide (TP/FP) MCP manifest Stdio wrapper SARIF Integrate Findings queue Vote TP/FP on findings to calibrate the engine.

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