yt-dlp/yt-dlp

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149 of your 316 findings came from Repobility's proprietary detections. ✓ Repobility tags below mark them.

Scan timing: clone 4.37s · analysis 27.96s · 12.4 MB · GitHub API rate-limit (preflight)

https://github.com/yt-dlp/yt-dlp · scanned 2026-06-04 22:00 UTC (15 hours, 26 minutes ago) · 10 languages

667 findings (295 legacy + 372 scanner) 3rd percentile · Python · large (100-500K LoC) Scanner says 79 (lower by 29)

File as issue Image v2 schema

UNIFIED Repobility · multi-layer engine · AI coders

Complete repo analysis

Last scanned 15 hours, 25 minutes ago · v2 · 481 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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100.0% cov · 186 gaps

Score breakdown â 2026-05-18-v5

Component	Sub-score	Weight	Contribution
`structure_score`	60.0	0.15	9.00
`security_score`	0.0	0.25	0.00
`testing_score`	43.0	0.20	8.60
`documentation_score`	93.6	0.15	14.04
`practices_score`	91.0	0.15	13.65
`code_quality`	45.0	0.10	4.50
Overall		1.00	49.8

Severity distribution — click a segment to filter

Active filters: excluding tests × Reset all

Severity: Critical 102 High 129 Medium 53 Low 140 9-Layer: Software Security Quality Integrity Frontend Hardware Data Network Cicd Source: Legacy 295 9-layer 186 Crowd 0 Layer: Quality 277 Software 39 Cicd 26 Security 131 Api 1 Network 4 Hardware 2 Frontend 1

Exclude dismissed / FP Exclude test files

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 162 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 Public web service has no security.txt .well-known/security.txt
medium [SEC127] AI agent stub — TODO: implement / pass placeholder body: Function body… yt_dlp/networking/websocket.py:18
medium [SEC127] AI agent stub — TODO: implement / pass placeholder body: Function body… yt_dlp/networking/_helper.py:163
medium [SEC127] AI agent stub — TODO: implement / pass placeholder body: Function body… yt_dlp/extractor/motherless.py:169
medium [ERR001] Silent Exception Swallowing: Silently swallowing all exceptions hides … devscripts/tomlparse.py:129
medium [COMP001] High cognitive complexity: Function `sort_ies` has cognitive complexi… devscripts/make_lazy_extractors.py:81
medium Bare except continues silently yt_dlp/networking/__init__.py:37
medium Bare except continues silently yt_dlp/networking/__init__.py:30
medium Bare except continues silently yt_dlp/networking/__init__.py:23
medium Bare except continues silently yt_dlp/networking/_requests.py:244
medium Bare except continues silently yt_dlp/downloader/fc2.py:27
medium Bare except continues silently yt_dlp/downloader/niconico.py:79

View all fragile runtime findings →

Legacy markers 30 findings

What it is: TODO, FIXME, XXX, HACK comments. Often indicate a known-broken path the author meant to fix.

Why it matters: Each marker is an unfinished thought. Production code shouldn't ship with debt that's documented but not tracked.

How AI causes it: AI mirrors the style of the codebase, so existing TODOs propagate into new code.

Fix approach: Convert each into a ticket. Delete the comment when the ticket lands. Use a pre-commit hook to block new TODOs without an issue link.

12 matching findings on this repo

low Legacy-named symbol `test_copy` in test/test_networking.py:2048
low Legacy-named symbol `_open_database_copy` in yt_dlp/cookies.py:164
low Legacy-named symbol `check_deprecated` in yt_dlp/YoutubeDL.py:749
low Legacy-named symbol `ctx_copy` in yt_dlp/downloader/fragment.py:489
low Legacy-named symbol `info_copy` in yt_dlp/downloader/__init__.py:6
low Legacy-named symbol `info_copy` in yt_dlp/downloader/websocket.py:16
low Legacy-named symbol `info_copy` in yt_dlp/postprocessor/common.py:21
low Legacy-named symbol `ie_copy` in yt_dlp/postprocessor/ffmpeg.py:1163
low Legacy-named symbol `format_copy` in yt_dlp/extractor/rai.py:162
low Legacy-named symbol `wim_v1` in yt_dlp/extractor/wimbledon.py:49
low Legacy-named symbol `abrjson_v2` in yt_dlp/extractor/fujitv.py:52
low Legacy-named symbol `s_v2` in yt_dlp/extractor/brainpop.py:132

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Commented-out code 30 findings

What it is: Lines of source that were intentionally disabled but never deleted.

Why it matters: Git already remembers history — commented code rots, becomes wrong, and adds noise to diffs.

How AI causes it: AI sometimes comments out broken code instead of fixing it. Reviewers approve out of inertia.

Fix approach: Delete. Trust `git log`. If you really need to remember, save it in a notes file under `docs/`.

12 matching findings on this repo

info Commented-code block (13 lines) in test/test_subtitles.py:112
info Commented-code block (5 lines) in yt_dlp/webvtt.py:149
info Commented-code block (6 lines) in yt_dlp/__init__.py:693
info Commented-code block (8 lines) in yt_dlp/cookies.py:941
info Commented-code block (6 lines) in yt_dlp/YoutubeDL.py:1957
info Commented-code block (8 lines) in yt_dlp/networking/_urllib.py:129
info Commented-code block (5 lines) in yt_dlp/downloader/external.py:642
info Commented-code block (5 lines) in yt_dlp/downloader/f4m.py:293
info Commented-code block (5 lines) in yt_dlp/downloader/http.py:120
info Commented-code block (5 lines) in yt_dlp/postprocessor/modify_chapters.py:139
info Commented-code block (5 lines) in yt_dlp/extractor/wppilot.py:130
info Commented-code block (5 lines) in yt_dlp/extractor/zdf.py:532

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Config drift 3 findings

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.

3 matching findings on this repo

high [SEC043] Secret stored in Odoo ir.config_parameter — broadly readable: ir.confi… yt_dlp/extractor/gofile.py:65
high [SEC043] Secret stored in Odoo ir.config_parameter — broadly readable: ir.confi… yt_dlp/extractor/dropbox.py:62
high [SEC043] Secret stored in Odoo ir.config_parameter — broadly readable: ir.confi… yt_dlp/extractor/ciscowebex.py:42

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