How does EchelonGraph detect OWASP LLM Top 10 LLM08 violations?

EchelonGraph automatically detects OWASP LLM Top 10 LLM08 violations using scanner rule OWASP-LLM-008. The scanner checks cloud infrastructure configurations in real-time and flags non-compliant resources.

What audit questions are asked for OWASP LLM Top 10 LLM08?

Are any of your vector DBs exposed to the public internet? What authentication is required on each vector DB? Is per-tenant index isolation enforced? How is indexed content sanitised before embedding?

🧠OWASP LLM Top 10 LLM08Rule: OWASP-LLM-008high

Vector and Embedding Weaknesses

Description

Vulnerabilities in vector DBs (Milvus, Weaviate, Qdrant, Pinecone, Chroma) and RAG embeddings: injection via indexed content, cross-tenant leakage, unauthenticated access.

⚠️ Risk Impact

Vector DBs are the new RDBMS for LLM apps — but security maturity is far behind. The Shadow AI Radar finds thousands of internet-exposed Milvus/Weaviate instances; many require no authentication.

🔍 How EchelonGraph Detects This

OWASP-LLM-008Automated scanner rule

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

🖥️ Manual Verification

terminal

# Check vector DB authentication
curl -I http://your-milvus-host:19530/v1/health  # Should require auth

🔧 Remediation

Authenticate vector DBs. Segregate per-tenant indexes. Sanitise content before embedding (treat indexed content as semi-trusted). Encrypt vector data at rest. Restrict vector DB to internal networks.

💀 Real-World Attack Scenario

Wiz Research (Jan 2025) found DeepSeek's ClickHouse database (used for chat logs + vector embeddings) exposed to the internet without authentication. 1M+ chat logs and API keys leaked in <60 minutes of internet scanning. The DeepSeek case is the canonical 2025 vector/embedding security incident.

💰 Cost of Non-Compliance

DeepSeek API exposure (Jan 2025): public-disclosure-level reputational + reg-probe exposure. Avg vector-DB exposure incident in 2024: $1.8M (Wiz AI Threat Report).

📋 Audit Questions

1.Are any of your vector DBs exposed to the public internet?
2.What authentication is required on each vector DB?
3.Is per-tenant index isolation enforced?
4.How is indexed content sanitised before embedding?

🎯 MITRE ATT&CK Mapping

T1530 — Data from Cloud StorageMITRE_ATLAS-AML.T0040 — ML IP Theft

🏗️ Infrastructure as Code Fix

main.tf

resource "kubernetes_network_policy" "vector_db_internal_only" {
  metadata { name = "vector-db-no-public-ingress"; namespace = "ai" }
  spec {
    pod_selector { match_labels = { app = "milvus" } }
    ingress {
      from {
        namespace_selector { match_labels = { tier = "ai-internal" } }
      }
    }
  }
}

⚡ Common Pitfalls

⛔Deploying Milvus / Weaviate / Qdrant with default-no-auth config
⛔Single global index for multi-tenant data
⛔Skipping content sanitisation because 'it's our content'

📈 Business Value

Vector DB hardening prevents the most-frequent 2025 AI infra incident pattern. Reduces avg incident cost by 70%+.

⏱️ Effort Estimate

Manual

2-3 weeks for cluster-wide vector DB audit + hardening

With EchelonGraph

EchelonGraph's Shadow AI Radar detects exposed vector DBs; provides hardening guidance

🔗 Cross-Framework References

MITRE_ATLAS-AML.T0010

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