detecting（detecting 搜索）

Detectability Analysis (LINDDUN D1)

Analyze source code for detectability threats where an observer can determine that a user is interacting with a system, that a record exists, or that a specific action was performed -- even without accessing the content itself. Privacy is compromised when the mere existence of an interaction or record reveals sensitive information.

Supported Flags

Read ../../shared/schemas/flags.md for full flag documentation. This skill supports all cross-cutting flags.

Framework Context

LINDDUN D1 -- Detectability

Detectability occurs when an adversary can determine that an item of interest exists, even without accessing its content. Read ../../shared/frameworks/linddun.md for the full LINDDUN framework reference including detectability patterns, timing side channels, and traffic analysis threats.

Privacy Property Violated: Undetectability / Unobservability

STRIDE Mapping: Information Disclosure (detectability focuses on existence proofs and metadata patterns rather than direct content access)

Workflow

Step 1 -- Determine Scope

1. Parse --scope flag (default: changed).
2. Resolve to a concrete file list.
3. Filter to relevant files: API handlers, error handling middleware, presence or status indicators, encrypted message handling, notification systems, and cache management.
4. Prioritize files containing: HTTP error responses, user existence checks, online status indicators, read receipts, and response timing logic.

Step 2 -- Analyze for Detectability Patterns

Read each scoped file and assess what an observer can detect about system usage or data existence:

1. Check error response uniformity: Determine whether different error codes or messages reveal record existence (e.g., "user not found" vs. "wrong password").
2. Assess timing consistency: Look for code paths where response time differs based on record existence (cache hit vs. database miss).
3. Examine presence indicators: Find online status, typing indicators, read receipts, or activity signals that lack opt-out mechanisms.
4. Check message padding: Determine whether encrypted messages have uniform size or leak content type through size variation.
5. Evaluate enumeration surfaces: Identify endpoints that allow probing for existence of users, records, or resources.

At --depth deep or --depth expert, model the full observable surface and determine what an adversary can infer from response patterns and metadata.

Step 3 -- Report Findings

Output findings per ../../shared/schemas/findings.md. Each finding needs: DTCT-NNN id, title, severity (based on sensitivity of detectable information and ease of observation), location with snippet, description of what can be detected and through which channel, impact (what an observer infers), fix (uniform responses, constant-time ops, or padding), and CWE/LINDDUN references.

Analysis Checklist

1. Do error responses differentiate "not found" from "access denied" for user lookups?
2. Can an observer enumerate valid usernames, emails, or account IDs?
3. Do response times vary based on whether a record exists (timing oracle)?
4. Are there presence indicators or read receipts without opt-out?
5. Do encrypted messages vary in size, revealing content type or length?
6. Can push notification patterns reveal when a user receives messages?
7. Do API rate limit responses differ for authenticated vs. non-existing users?
8. Can traffic analysis reveal which features or services a user accesses?

What to Look For

1. User enumeration via error messages: Different responses for existing vs. non-existing accounts.

- Grep: user not found|invalid username|no such user|email not registered|account does not exist

1. Timing oracles: Early-return patterns that reveal record existence.

- Grep: if.*!user.*return|if.*notFound.*return|findOne.*then.*404|catch.*404

1. Presence and activity indicators: Online status, typing, and read receipt systems.

- Grep: isOnline|online_status|lastSeen|last_active|typing.*indicator|read.*receipt|seen.*at

1. Account enumeration endpoints: Registration, password reset, or login that reveal existence.

- Grep: already registered|email.*taken|username.*exists|account.*already|duplicate.*email

1. Unpadded encrypted content: Variable-length encrypted messages.

- Grep: encrypt\(|cipher\.update|AES.*encrypt|padding|PKCS|NoPadding

1. Observable API patterns: Sequential IDs or predictable resource identifiers.

- Grep: autoIncrement|SERIAL|sequence|nextval|uuid.*v1|ObjectId

1. Notification metadata leakage: Push notification patterns revealing user behavior.

- Grep: sendNotification|pushNotif|FCM|APNs|webpush|notify\(.*user

Regulatory Mapping

Output Format

Use finding ID prefix DTCT (e.g., DTCT-001, DTCT-002).

All findings follow the schema in ../../shared/schemas/findings.md with:

• references.cwe: CWE-203 (Observable Discrepancy) or CWE-208 (Observable Timing Discrepancy)
• references.owasp: A05:2021 (Security Misconfiguration -- information leakage in responses)
• metadata.tool: "detecting"
• metadata.framework: "linddun"
• metadata.category: "D1"

Summary table after all findings:

| Detectability Pattern       | Critical | High | Medium | Low |
|-----------------------------|----------|------|--------|-----|
| User enumeration            |          |      |        |     |
| Timing side channels        |          |      |        |     |
| Presence indicators         |          |      |        |     |
| Account enumeration         |          |      |        |     |
| Message size leakage        |          |      |        |     |
| Notification patterns       |          |      |        |     |

Followed by: top 3 priorities, enumeration surface map, and overall assessment.