Pruebas de seguridad para aplicaciones LLM

Pruebas de seguridad para aplicaciones LLM

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Minimice los riesgos de sus LLMs

Nuestro servicio de pentesting en LLMs está diseñado para evaluar la resistencia y seguridad de tus modelos y sistemas, protegiendo su integridad y la confianza en tus resultados.

OWASP Top 10 for
LLM Applications

Utilizamos OWASP que ofrece una guía sobre las vulnerabilidades más comunes y peligrosas en LLMs y permite a los desarrolladores tomar medidas preventivas para asegurar sus modelos de aprendizaje.

This manipulates a large language model (LLM) through crafty inputs, causing unintended actions by the LLM. Direct injections overwrite system prompts, while indirect ones manipulate inputs from external sources.

This occurs when LLM training data is tampered,
introducing vulnerabilities or biases that compromise
security, effectiveness, or ethical behavior. Sources
include Common Crawl, WebText, OpenWebText, & books

LLM application lifecycle can be compromised by
vulnerable components or services, leading to security attacks. Using third-party datasets, pre- trained models, and plugins can add vulnerabilities.

LLM plugins can have insecure inputs and insufficient
access control. This lack of application control makes
them easier to exploit and can result in consequences like remote code execution.

Systems or people overly depending on LLMs without
oversight may face misinformation, miscommunication, legal issues, and security vulnerabilities due to incorrect or inappropriate content generated by LLMs.

This vulnerability occurs when an LLM output is accepted without scrutiny, exposing backend systems. Misuse may lead to severe consequences like XSS, CSRF, SSRF, privilege escalation, or remote code execution

Attackers cause resource-heavy operations on LLMs,
leading to service degradation or high costs. The
vulnerability is magnified due to the resource-intensive nature of LLMs and unpredictability of user inputs.

LLMs may inadvertently reveal confidential data in their responses, leading to unauthorized data access, privacy violations, and security breaches. Its crucial to implement data sanitization and strict user policies to mitigate this

LLM-based systems may undertake actions leading to unintended consequences. The issue arises from
excessive functionality, permissions, or autonomy granted to the LLM-based systems

This involves unauthorized access, copying, or exfiltration of proprietary LLM models. The impact includes economic losses, compromised competitive advantage, and potential access to sensitive information

Integridad de los Resultados

Asegura que los modelos de IA proporcionen respuestas confiables y estén libres de manipulaciones externas.

Protección de la Privacidad

Resguarda la información sensible que se maneja en modelos de IA, cumpliendo con normativas de privacidad y seguridad.

Preparación Ante Ataques Emergentes

Identificamos posibles puntos de fallo en tus sistemas para protegerlos ante métodos de ataque sofisticados y novedosos.