sfcc-fraud-prevention — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited sfcc-fraud-prevention (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Fraud prevention in SFCC is a system, not a single feature. This skill focuses on practical developer responsibilities:
[ ] Start at the edge: eCDN rules (block obvious bad traffic)
[ ] Enforce payment checks (AVS/CVV/3DS where available)
[ ] Add server-side validation (never trust the client)
[ ] Add velocity + behavior checks using minimal state
[ ] Ensure retention + purge jobs for any temporary state objects
[ ] Log decisions carefully (no PII leakage)Common families you’ll see in data:
Your job is to translate these into signals you can observe and validate.
| Signal type | Examples |
|---|---|
| Data mismatches | IP geolocation vs billing/shipping country; name/address anomalies |
| Velocity patterns | many attempts from same IP/email/device; repeated failures then success |
| Order characteristics | unusually large order; high-risk SKUs; rush shipping |
| Identity indicators | disposable email; gibberish names; repeated address reuse |
Where to implement depends on architecture:
A practical approach:
If you store historical signals:
If you must store per-session state:
See also: sfcc-logging and sfcc-security.
Third-party fraud tools can be the correct choice when:
Build custom logic when:
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.