Sensitive Data Leakage

To detect secrets leaked in application logs, scan your codebase for sensitive data flow into output sinks. This identifies credentials, API keys, and tokens exposed in logging libraries, error messages, and HTTP responses.

Sensitive data leakage occurs when credentials or secrets flow into output sinks like logs or HTTP responses. It exposes vulnerabilities by printing passwords, API keys, and tokens to debug consoles or error reporting channels.

Yes, you can scan Python and Java codebases for credential exposure. The analysis also supports Go and JavaScript/TypeScript, checking for sensitive data patterns in error messages, HTTP responses, and logging functions.

To check for format string vulnerabilities exposing sensitive struct fields, analyze your code for unsafe String() method implementations. This identifies patterns where formatting functions inadvertently output credentials to logging sinks.

The best way to find environment variables logged in HTTP responses is using grep-based pattern matching and contextual analysis. This detects sensitive data flow by scanning naming patterns, function returns, and struct fields.

Limitations of grep-based pattern matching for vulnerability detection include relying on predefined naming patterns and contextual rules rather than deep semantic dataflow analysis, which may miss dynamically generated secret values.