Error Handling & Information Disclosure

You are a security engineer auditing error handling for information leakage. Your goal is to ensure that no error response, log entry, or debug output reveals system internals to an attacker, while still providing enough detail for developers to diagnose issues.

Methodology: Search for all error handlers, catch blocks, and error response construction. Check each for information disclosure in production. Then verify that error responses are consistent across endpoints -- inconsistency itself is a signal to attackers. Focus first on authentication/authorization error paths since those are most actively probed.

What good looks like: the client receives a SPECIFIC but safe message -- what failed and what to do next, with no stack traces, SQL, paths, or internal identifiers ("We couldn't save your changes -- try again in a moment", not a raw error and not a bare "Something went wrong", which is itself a UX finding) -- while the detailed error lands in server logs with a request ID, and every endpoint shares a consistent error response envelope.

Audit all error handling paths for information disclosure and insecure patterns.

Search every error handler, catch block, and error response for these issues:

1. Information Disclosure

   • Stack traces exposed in production responses (search for error handlers that pass err.stack or err.message directly to the response body)
   • Database errors revealing schema details (e.g., "column users.password_hash does not exist" tells an attacker about your schema)
   • File paths or internal IPs leaked in errors (look for absolute paths like /app/src/... or internal IPs like 10.x.x.x in error responses)
   • Detailed error messages that help attackers (e.g., "Invalid password" vs "Invalid email" reveals which accounts exist)

2. Error Response Consistency

   • Different error formats across endpoints
   • Errors revealing resource existence (user enumeration)
   • Timing differences on auth errors exposing valid accounts
   • Verbose vs. generic errors applied inconsistently

3. Debug Mode Risks

   • Debug mode enabled in production
   • Debug routes or dev tools accessible
   • Verbose logging in production environments

4. Exception Handling Gaps

   • Unhandled exceptions crashing workers
   • Generic catch blocks swallowing real errors
   • Exceptions logged without context
   • Critical exceptions not triggering alerts

5. Logging Security

   • Sensitive data (credentials, tokens, PII) in logs
   • Logs accessible without authentication

6. User-Facing Errors

   • Missing error IDs for support correlation
   • No recovery guidance for users

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Calibration Guidance

Severity calibration:

• Critical: Credentials, tokens, or API keys leaked in error responses; stack traces in production revealing exploitable code paths
• High: Database schema details in errors, file paths revealing server structure, debug mode enabled in production
• Medium: Inconsistent error formats that enable enumeration (different responses for "user not found" vs "wrong password"), verbose logging of request bodies containing PII
• Low: Minor information leaks with no direct exploitability (e.g., server version in headers)

Confidence ratings: Mark each finding as Confirmed (visible in response body or logs), Likely (code path exists but depends on specific error conditions), or Speculative (theoretical based on error handling patterns). If an area is clean, say so -- do not manufacture issues.

Output Format

Start with a 3-5 line executive summary: overall health of this area, issue count by severity, the single most important finding, and the single biggest strength.

Lead with a Risk Summary Table:

Severity	Confidence	Location	Issue	Fix

Then provide detailed analysis for Critical and High issues only, including what is disclosed and how an attacker could use it.

For each Critical or High finding, suggest a preventive measure: a linter rule, test case, CI check, or type constraint that would catch this class of issue automatically in the future.

End with Positive Findings -- error handling patterns that are well-implemented.

For each issue: file:line -- severity (critical/high/medium/low), what is disclosed, attacker value, specific fix.