LLM02:2025: Sensitive Information Disclosure

LLMs leak training data, system prompts, customer PII, secrets, or proprietary logic through inference or memorized content.

Found by probing model responses with targeted extraction prompts to check for training-data leakage, memorized secrets, or system-prompt disclosure.

LLM02:2025 maps to CWE-200, CWE-359.

TurboPentest's automated black-box pentest actively probes for Exposure of Sensitive Information to an Unauthorized Actor using Vuln Scanner's exposure templates (exposed configuration files, admin panels, API keys, .env and .git paths), with no source code required. Connecting a GitHub repo adds white-box confirmation from IntegSec's Opengrep SAST rule pack, which carries 8 dedicated rules for this weakness. For AI/LLM targets, Paladin AI additionally probes model responses for sensitive-information disclosure, such as leaked system prompts, training-data fragments, or credentials surfaced in generated output.