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Informational 18 Monero

feat(shopinbit): add car request payload and invoice recovery to client

Public commit record

What the developer wrote

Authored by sneurlax

62/100 · Adequate
feat(shopinbit): add car request payload and invoice recovery to client
✓ Specific, descriptive subject✓ Names a concrete action or component✓ Uses a recognizable type or scope! No meaningful explanatory body
The short version

What changed, and why it matters

This commit adds two new features to the ShopInBit integration in Stack Wallet: it lets users attach a car-research request to a fee invoice, and it lets them retrieve any unpaid car-research invoices so they can finish paying. The code itself is a normal feature addition and does not appear to introduce an obvious security vulnerability. The main thing to watch is that the new API calls send personal/pseudonymous data and payment links over the network, so they rely on the existing HTTPS and authentication plumbing being correct.

Recommended action

Treat as a routine feature commit. As a defensive check, reviewers should verify that _request/_requestRaw enforces TLS certificate validation, attaches authentication correctly, and does not log the new request body or payment links. Consider adding server-response schema validation and graceful handling of unexpected JSON shapes to harden the new endpoints.

Security signals we found

01

New network API surface added (POST /car-research/invoice with optional request body, GET /car-research/invoices/current)

02

User-provided strings (customerPseudonym, comment, deliveryCountry) serialized to JSON and sent to backend

03

Payment links parsed from server response and stored in Map<String, String>

04

Status normalization helper uses lowercase string comparison for invoice lifecycle states

05

No input validation/sanitization visible in the new model code

06

No secrets, tokens, or credentials added in the diff

Risk score

Why this scored 18/100

Our methodology →
Potential impact 2/30
Exploitability 2/25
Stealth signal 1/15
Affected reach 3/15
Confidence 7/10
Evidence quality 3/5
Human-validated context

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