Individual skill
application-data-exposure-testing
Distinguish real unauthorized data exposure from harmless metadata - response field differentials by role and tenant, over-fetching, alternate serializers and exports, error responses, notifications, audit logs, cached content - requiring meaningful confidentiality or authorization impact to report.
internal/tools/skills/data/application-security/application-data-exposure-testing/SKILL.md
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- Observed behavior
- Instructions only
- Last checked
- Nov 9, 2026
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- 1,324
- Repository last updated
- Nov 9, 2026
- License
- Apache-2.0
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- cybersecurity Repository topic
- security Repository topic
- bug-bounty Repository topic
- automation Repository topic
- autonomous-pentesting Repository topic
- ai-agent Repository topic
- ai-security Repository topic
- ethical-hacking Repository topic
- golang Repository topic
- penetration-testing Repository topic
- data-exposure Declared skill tag
- information-disclosure Declared skill tag
- over-fetching Declared skill tag
- serializer Declared skill tag
- pii Declared skill tag
- differential Declared skill tag
- writing Derived category
- data Derived category
- security Derived category
- testing Derived category
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SKILL.mdcore · 7,889 bytes
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# Application Data Exposure Testing
## Purpose
Applications leak data through many channels beyond "the endpoint returned a field": nested objects, alternate serializers, exports, error responses, notifications, audit logs, caches. Most such leakage is harmless metadata. This skill finds the subset with meaningful confidentiality or authorization impact - by asking, for every response, what THIS identity should have received versus what it actually received.
## Entry Conditions
- You can observe the same application object or operation from at least two identity contexts (roles, tenants, or anonymous)
- OR: the application has export/report/serialization surfaces beyond the primary API
## Differential Reasoning (the core method)
For every response channel: