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embedguard

neerazz/embedguard

Cross-Layer Detection and Provenance Attestation for Adversarial Embedding Attacks in RAG Systems

GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026

37views this month

0 stars0 forksLast push Aug 18, 2026 Python MIT

Decision brief

Embedguard is a Python tool designed for detecting and attesting provenance against adversarial embedding attacks in RAG systems, emphasizing AI safety and security.

Good fit when

When you need to ensure the integrity of embeddings in RAG systems against adversarial attacks.
If your project involves AI safety and security, particularly in the context of prompt injection and provenance verification.

Avoid when

If your focus is on general-purpose AI model evaluation without a specific need for adversarial embedding attack detection.
When your application does not involve RAG systems or you are not concerned with embedding security and provenance.

Requirements:: Installation requires Python and pip.; Development mode installation is supported.

Observed Sep 18, 2026 · Source: enrich:decision_facts

embedguard alternatives Trust report

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Maintenance and security

Full trust report

Maintenance: Steady (30d since push); As of Sep 18, 2026
Provenance: Not a fork · Personal account; As of Sep 18, 2026
Security (OSV): 4 low (4 low); As of Sep 18, 2026

Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.

Install

pip install embedguard

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Evidence and technical details

Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.

Overview

A tool for detecting and attesting provenance against adversarial embedding attacks in RAG systems, focusing on AI safety and security.

Capability facts

Deploy: Self-host
Source: dockerfile:Dockerfile · Sep 18, 2026
Docker: Dockerfile present
Source: dockerfile:Dockerfile · Sep 18, 2026
CLI: CLI entrypoint
Source: pyproject.toml:[project.scripts] · Sep 18, 2026
Languages: python
Source: github.language+pyproject.toml · Sep 18, 2026

Categories

Evaluation & Observability LLM Frameworks Vector Databases

Compatibility

Sourced claims from the README excerpt - not unsourced marketing copy.

Python runtimePython

Source: README excerpt (regex_v1, Sep 18, 2026)

pip install -e .

Tags

ai-safety embedding-attacks llm llm-security prompt-injection provenance rag rag-security

README

Quick start License This project is licensed under the MIT License see the LICENSE file for details.

For agents

This page has a .md twin and JSON over the API.

/tools/neerazz-embedguard.md GET /api/graphcanon/tools/neerazz-embedguard GET /api/graphcanon/graph?tool=neerazz-embedguard

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