How does EchelonGraph detect OWASP LLM Top 10 LLM02 violations?

EchelonGraph automatically detects OWASP LLM Top 10 LLM02 violations using scanner rule OWASP-LLM-002. The scanner checks cloud infrastructure configurations in real-time and flags non-compliant resources.

What audit questions are asked for OWASP LLM Top 10 LLM02?

How is PII redacted from prompts? What output filters apply on regulated data types? Have you tested for training-data memorisation? Show me a recent PII-leak prevention rule update.

🧠OWASP LLM Top 10 LLM02Rule: OWASP-LLM-002critical

Sensitive Information Disclosure

Description

LLM inadvertently discloses sensitive information: PII memorised from training data, business secrets in system prompts, customer data accumulated in conversation history.

⚠️ Risk Impact

LLMs that handle sensitive data have multiple pathways to disclose it — training-data memorisation, system-prompt leakage, cross-session leakage, and downstream API responses.

🔍 How EchelonGraph Detects This

OWASP-LLM-002Automated scanner rule

EchelonGraph's Tier 1 Cloud Scanner automatically checks for this condition across all connected cloud accounts. Violations are flagged as critical-severity findings with remediation guidance.

🔧 Remediation

Redact PII before prompting. Apply output filters on regulated entity types (SSN, credit cards, PHI). Document training data; never train on production customer data without DP. Refuse PII queries via system-prompt + classifier.

💀 Real-World Attack Scenario

ChatGPT (Mar 2023) had a Redis bug that leaked snippets of other users' conversations in the conversation history. ~1.2% of ChatGPT Plus subscribers had payment data potentially exposed. The cause was infrastructure not training-data — but the impact was a textbook LLM02 violation.

💰 Cost of Non-Compliance

ChatGPT Mar 2023 incident: regulator inquiries from Italy, France, Germany. Italy temporarily banned ChatGPT. Avg LLM-sensitive-disclosure incident cost in 2024: $1.4M (Wiz).

📋 Audit Questions

1.How is PII redacted from prompts?
2.What output filters apply on regulated data types?
3.Have you tested for training-data memorisation?
4.Show me a recent PII-leak prevention rule update.

🎯 MITRE ATT&CK Mapping

MITRE_ATLAS-AML.T0024 — Model Inversion

⚡ Common Pitfalls

⛔Training on production customer data without DP
⛔Logging full prompts (with PII) for debugging
⛔System prompts containing API keys or business secrets

📈 Business Value

Sensitive-information defence prevents the highest-stakes LLM incidents — material for healthcare, finance, and any GDPR-scoped LLM deployment.

⏱️ Effort Estimate

Manual

3-4 weeks for redaction pipeline + output filtering + DP integration

With EchelonGraph

EchelonGraph ships PII detection + output filtering middleware for LLM endpoints

🔗 Cross-Framework References

GDPR-Art32HIPAA-164.312(e)MITRE_ATLAS-AML.T0024

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