Individual skill
sensitive-information-disclosure
Hunt LLM sensitive-information disclosure (OWASP LLM02:2025) — leakage of PII, secrets, internal source, model details, and other-tenant data through model outputs, training-data extraction, or retrieval-side joins.
packages/decepticon/decepticon/skills/standard/analyst/sensitive-information-disclosure/SKILL.md
Source commit df879550544d36b6b1e69062f05adae28537adf2
What was checked
- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- No
- Repository scan complete
- No
- Skill scan complete
- No
- Observed behavior
- Not checked yet
- Last checked
- Nov 8, 2026
These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.
Install this skill
Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.
An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.
Repository context
- Repository stars (not skill ratings)
- 6,567
- Repository last updated
- Nov 7, 2026
- License
- Apache-2.0
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- agent Repository topic
- ai Repository topic
- cybersecurity Repository topic
- hacking Repository topic
- langchain Repository topic
- langgraph Repository topic
- llm Repository topic
- generative-ai Repository topic
- pentesting Repository topic
- pentest Repository topic
- data Derived category
- security Derived category
- security-application Derived category
Files and excerpt
File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.
Browse files
Read excerpt
# LLM Sensitive Information Disclosure (LLM02:2025)
LLMs are state machines that emit anything in their context. When that
context includes secrets (API keys, customer PII, cross-tenant rows,
embeddings indexed from internal docs), the model is one creative
prompt away from emitting them verbatim. Disclosure usually beats
prompt injection on bug bounty triage time because the proof is in the
output itself.
## 1. Recognition signals
- The product offers a "chat with your data" or RAG interface.
- The vendor advertises "memory" or "long-term context" across sessions.
- Logs / debug pages echo prompt or retrieved chunks back to the UI.