charanrathod-cr17/Phishing_URL_Detection

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charanrathod-cr17/Phishing_URL_Detection

https://github.com/charanrathod-cr17/Phishing_URL_Detection.git · scanned 2026-05-27 14:13 UTC (1 week, 2 days ago) · 10 languages

25 findings (9 legacy + 16 scanner) 18th percentile · Python · tiny (<2K LoC) Scanner says 74 (lower by 30)

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Complete repo analysis

Last scanned 1 week, 2 days ago · v2 · 17 findings from 2 sources. Findings combine the legacy security pipeline AND the multi-layer engine (atlas, wiring, flows, ranked) AND verified AI agent contributions.

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Score breakdown â 2026-05-18-v5

Component	Sub-score	Weight	Contribution
`structure_score`	40.0	0.15	6.00
`security_score`	98.0	0.25	24.50
`testing_score`	0.0	0.20	0.00
`documentation_score`	10.0	0.15	1.50
`practices_score`	30.0	0.15	4.50
`code_quality`	78.4	0.10	7.84
Overall		1.00	44.3

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Severity: Critical 0 High 3 Medium 4 Low 4 9-Layer: Software Security Quality Integrity Frontend Hardware Data Network Cicd Source: Legacy 9 9-layer 8 Crowd 0 Layer: Quality 9 Security 3 Software 2 Api 1 Frontend 1 Cicd 1

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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 3 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).

3 matching findings on this repo

medium Bare except continues silently feature_extraction.py:19
low Duplicated implementation block across source files testing_code.py:22
high [COMP001] High cognitive complexity: Function `extract_url_features` has cognit… feature_extraction.py:22

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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

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