Skip to main content

vamp-rag-audit

RAG and agentic AI security auditor — VampSecure Labs · VampSecure Studios

Auditor de seguridad activo para sistemas de Retrieval-Augmented Generation (RAG) e IA agéntica. Detecta indirect prompt injection, context poisoning, data exfiltration, embedding attacks, context window overflow y retrieval manipulation.

Para uso exclusivo en pruebas de penetración autorizadas.


Instalación

pip install vamp-rag-audit

O desde el repositorio:

pip install .

Uso

# Auditoría básica contra un endpoint RAG/LLM
vamp-rag-audit probe http://localhost:8080/api/query

# Con API key
vamp-rag-audit probe http://api.example.com/chat --api-key sk-xxx

# Extraer endpoints desde spec OpenAPI
vamp-rag-audit probe --spec openapi.yaml

# Output JSON para integración CI/CD
vamp-rag-audit probe http://localhost:8080/api/query --format json

# Generar informe HTML
vamp-rag-audit probe http://localhost:8080/api/query --output informe.html

# Ejecutar solo checks específicos
vamp-rag-audit probe http://localhost:8080/api/query --checks RAG-001 RAG-002 RAG-003

# Filtrar por severidad mínima
vamp-rag-audit probe http://localhost:8080/api/query --severity HIGH

Checks incluidos

ID Severidad Descripción
RAG-001 CRITICAL Indirect Prompt Injection vía contexto recuperado
RAG-002 CRITICAL Context Poisoning con Unicode Tags invisibles
RAG-003 HIGH Data Exfiltration por manipulación de retrieval
RAG-004 HIGH Context Window Overflow
RAG-005 HIGH Source Attribution Bypass
RAG-006 MEDIUM Retrieval Query Injection (SQL/NoSQL)
RAG-007 MEDIUM Agentic Tool Invocation vía documento inyectado
RAG-008 MEDIUM Metadata Poisoning
RAG-009 LOW Information Disclosure vía Embeddings
RAG-010 LOW Missing Input Sanitization (XSS/HTML)

OWASP LLM Top 10 2025 — Mapping

  • LLM01 Prompt Injection → RAG-001, RAG-002, RAG-004, RAG-005, RAG-006, RAG-008
  • LLM06 Sensitive Information Disclosure → RAG-003, RAG-009
  • LLM07 Insecure Plugin Design → RAG-007
  • LLM08 Excessive Agency → RAG-005
  • LLM09 Misinformation / Insecure Output → RAG-010

Exit codes

  • 0 — Sin hallazgos CRITICAL/HIGH
  • 1 — Hallazgos detectados
  • 2 — Error de ejecución

Licencia

AGPL-3.0-only · © VampSecure Studios — VampSecure Labs Security Research Division

Metadata

Release files for vamp-rag-audit 1.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for vamp-rag-audit 1.0
File Size Uploaded Provenance
vamp_rag_audit-1.0.tar.gz 18.9 kB Without provenance Details

Built distribution (wheel)

Table of built distributions (wheels) for vamp-rag-audit 1.0
File Interpreter ABI Platform
vamp_rag_audit-1.0-py3-none-any.whl Python 3 none any Details

Total release size: 38.2 kB

Release files / vamp_rag_audit-1.0.tar.gz

Download URL vamp_rag_audit-1.0.tar.gz
Size 18.9 kB
Tags Source
SHA-256 checksum
How to use checksums
0ec4ec24ccd7ec6828d7f7d2f475fae3b2464f63fbc1e8cb2b0fc65bd9e35ce3
BLAKE2b-256 checksum
How to use checksums
08e9d16666a8e84101e65c8ad878b988fb5a27bb4aa88e49026ac43049e4c2ea
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.14.6

Release files / vamp_rag_audit-1.0-py3-none-any.whl

Download URL vamp_rag_audit-1.0-py3-none-any.whl
Size 19.4 kB
Tags Python 3
SHA-256 checksum
How to use checksums
5ff98dedc7adfddf082659fc54386311012a8f7bd3cf791e9dc8955bef844366
BLAKE2b-256 checksum
How to use checksums
2452e508bd2ecefe99e70d74a49a87e0662875543c543eb9ff009e238c67feca
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.14.6

Security for vamp-rag-audit 1.0

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Release history Release notifications | RSS feed

This release

1.0 This release

2 release files No provenance

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page