🛡️ Sentinel: [security improvement]

[tachyon-beep]

🚨 Severity: HIGH
💡 Vulnerability: Missing deserialization functions in taint propagation _SERIALISATION_SINKS, meaning UNKNOWN_RAW wouldn't correctly replace validated inputs for these sinks.
🎯 Impact: Could result in false negatives where malicious inputs bypass detection in these serialization functions.
🔧 Fix: Added shelve.open, dill.load, dill.loads, jsonpickle.decode, joblib.load, torch.load, and numpy.load to _SERIALISATION_SINKS in src/wardline/scanner/taint/variable_level.py. Updated a rule clean example, regenerated golden corpus, and confirmed tests pass.
✅ Verification: make test, make lint, and make typecheck all successfully pass.

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PR created automatically by Jules for task 12971695048007482136 started by @tachyon-beep

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[Copilot]

Pull request overview

This PR updates Wardline’s taint propagation to treat additional deserialization/representation-boundary functions as _SERIALISATION_SINKS, ensuring their outputs are conservatively modeled as UNKNOWN_RAW to avoid false negatives when validated inputs flow into these loaders.

Changes:

• Added several third-party and stdlib deserialization entry points (shelve.open, dill.load(s), jsonpickle.decode, joblib.load, torch.load, numpy.load) to _SERIALISATION_SINKS.
• Updated the PY-WL-106 rule’s clean example to stay clean under the stricter taint propagation.
• Documented the incident and learning in .jules/sentinel.md.

Reviewed changes

Copilot reviewed 3 out of 3 changed files in this pull request and generated 1 comment.

File	Description
src/wardline/scanner/taint/variable_level.py	Extends the core taint “representation boundary” sink set so these calls return UNKNOWN_RAW.
src/wardline/scanner/rules/untrusted_to_deserialization.py	Adjusts a clean example snippet to remain defect-free with updated taint behavior.
.jules/sentinel.md	Records the vulnerability/learning/prevention note for the added sinks.

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[chatgpt-codex-connector]

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[chatgpt-codex-connector]

Preserve safe load gates during taint propagation

Adding torch.load and numpy.load to this ungated serialization set makes _resolve_call return UNKNOWN_RAW for every call before the deserialization rule's gates can matter. In code that uses the documented safe forms (numpy.load(path)/allow_pickle=False or torch.load(..., weights_only=True)) and then returns or forwards the loaded value from a trusted function, this now triggers downstream taint defects such as PY-WL-101 even though UntrustedToDeserialization._accept_call explicitly suppresses those same calls as non-sinks at lines 153-155. These two entries need the same keyword gating in the propagation path rather than being treated as unconditional representation-boundary sinks.

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