google-research/google-research

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https://github.com/google-research/google-research.git · scanned 2026-05-16 13:30 UTC (3 weeks, 3 days ago) · 10 languages

57 findings 8/10 scanners ran 34th percentile · Python · medium (20-100K LoC)

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Complete repo analysis

14 actionable findings from 1 signal source. 37 repeated signals grouped for readability. Security checks, system graph analysis, and verified AI-agent feedback are merged into one review queue.

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

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Severity: Critical 0 High 4 Medium 1 Low 3 9-Layer: Software Security Quality Integrity Frontend Hardware Data Network Cicd Source: Security checks 14 System graph 0 Crowd 0 Layer: Quality 5 Security 8 Software 1

Exclude dismissed / FP Exclude test files

Showing 8 of 14 actionable findings. 51 raw detector signals were grouped into reader-sized issues. Click TP / FP to vote on a finding's accuracy — votes adjust the confidence weighting and improve detection across the platform.

high Security checks security Injection conf 0.85 [SEC004] SQL Injection Risk: String interpolation in SQL execution. Allows SQL injection.

Use parameterized queries: cursor.execute('SELECT * FROM t WHERE id = %s', [id]). For dynamic table or column names, choose identifiers from a hard-coded allowlist and keep values in parameters.

CardBench_zero_shot_cardinality_training/calculate_statistics_library/calculate_and_write_frequent_words.py:56

high Security checks security path traversal conf 0.80 [SEC013] Path Traversal — User Input in File Path: User-controlled input used in file path without sanitization. Allows reading arbitrary files.

Use os.path.realpath() and verify the path starts with your expected base directory. Use secure_filename() for uploads.

aav/model_training/train.py:276

Use os.path.realpath() and verify the path starts with your expected base directory. Use secure_filename() for uploads.

aav/util/inference_utils.py:67

medium Security checks quality Practices conf 1.00 [CFG006] Missing .gitignore: No .gitignore file. Risk of committing secrets and build artifacts.

Add a .gitignore appropriate for your language/framework.

low Security checks security Deserialization conf 1.00 3 occurrences [SEC007] Unsafe Deserialization: Unsafe deserialization can execute arbitrary code.

Use yaml.safe_load() instead of yaml.load(). Avoid pickle for untrusted data.

3 files, 3 locations

abps/py_hashed_replay_buffer.py:71

abstract_nas/utils.py:118

caltrain/glm_modeling/__init__.py:201

high Security checks quality Quality conf 0.82 Parallel implementation file sits beside a canonical file

Merge the intended change into the canonical file, update tests/imports, and delete the parallel implementation if it is not the active entry point.

CardBench_zero_shot_cardinality_training/generate_queries_library/query_generator_v2.py:1

low Security checks quality Quality conf 0.64 Duplicate top-level symbol appears in a patch-style file

Keep one authoritative implementation, update imports to point at it, and remove or rename the duplicate symbol.

CardBench_zero_shot_cardinality_training/generate_queries_library/query_generator_v2.py:1

low Security checks quality Quality conf 0.60 30 occurrences Duplicated implementation block across source files

Duplicate implementation blocks are maintenance debt. Keep them visible, but they are not a high-severity defect unless the duplicated logic is security-sensitive or drifting.

12 files, 19 locations

Algorithms_and_Hardness_for_Learning_Linear_Thresholds_from_Label_Proportions/bag_size_4/small_margin_4-sized_LLP-LTF.py:2, 8, 57

(3 hits)

COSTAR/runnables/train_msm.py:36, 40 (2 hits)

COSTAR/runnables/train_multi.py:56, 66 (2 hits)

COSTAR/runnables/train_rep_est.py:34, 39 (2 hits)

COSTAR/src/models/rep_est/CT_utils/encoder.py:11, 104 (2 hits)

COSTAR/src/models/rep_est/rep_est.py:84, 233 (2 hits)

Algorithms_and_Hardness_for_Learning_Linear_Thresholds_from_Label_Proportions/bag_size_3/small_margin_3-sized_LLP-LTF.py:2

Algorithms_and_Hardness_for_Learning_Linear_Thresholds_from_Label_Proportions/bag_size_4/large_margin_4-sized_LLP_LTF.py:2

duplicationquality

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