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maruakshay
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embedding-attack-security

Enforce validation and access controls to protect vector databases from embedding attacks.

4|Updated Apr 27, 2026
Search Tags:#inversion#access-control#poisoning#vector-databases#data-privacy#embedding-security#retrieval-security
One-click install
npx skills add https://github.com/maruakshay/mii-ai-security --skill embedding-attack-security
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: embedding-attack-security
Source: https://github.com/maruakshay/mii-ai-security/tree/main/skills/embedding-attack-security
Command: npx skills add https://github.com/maruakshay/mii-ai-security --skill embedding-attack-security

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Vector-space retrieval systems are vulnerable to poisoning, inversion, and cross-tenant leakage when embeddings are not properly guarded. This Skill provides a defense-in-depth blueprint for auditing and hardening embedding pipelines.

Core Features & Use Cases

  • Threat modeling and control lenses for ingestion, storage, and access of embeddings.
  • Concrete checks for proximity analysis, metadata governance, and index versioning.
  • Use Case: Security engineers harden a document-embedding index used across tenants and teams.

Quick Start

Run an embedding proximity check before indexing external content to block vector poisoning.

Frequently Asked Questions about embedding-attack-security

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I prevent embedding poisoning in a vector database?▼

Prevent embedding poisoning by running pre-ingestion validation and proximity checks on external content before indexing. This blocks malicious vectors from manipulating retrieval results and secures the pipeline against unauthorized data injection.

What is embedding inversion and how does it threaten data privacy?▼

Embedding inversion is an attack where original input content is reconstructed from stored vector representations. It threatens data privacy when access controls are weak, exposing sensitive information to unauthorized queries across tenants.

How do I stop cross-tenant leakage in vector retrieval systems?▼

Stop cross-tenant leakage by enforcing query-time authorization and embedding isolation. Applying strict access controls during retrieval ensures users only fetch vectors within their authorized tenant boundaries.

Can I audit and version my vector store to secure embeddings?▼

Yes, you can secure embeddings by maintaining versioned vector stores with auditing and metadata governance. This tracks index changes over time and provides guardrails to validate retrieval integrity.

Does this approach work for multi-tenant document embedding indexes?▼

Yes, this approach works for multi-tenant document embedding indexes by applying defense-in-depth controls. Security engineers can harden ingestion, storage, and access layers to protect shared indexes across teams.

What are the limitations of relying only on access control for embedding security?▼

Relying only on access control leaves gaps in ingestion and indexing phases. A robust embedding security posture requires additional validation, metadata governance, and proximity analysis to block poisoning and inversion attacks.

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