AI Pentesting
Evaluate the real security of generative AI solutions, LLMs, and agents, identifying technical vulnerabilities, abuse risks, and potential attack scenarios.
Specific tests for real AI systems
We analyze the model's behavior, prompts, context, guardrails, data sources, and connected tools to detect risks that traditional pentesting may not identify.
LLMs and AI applications
Evaluation of assistants, co-pilots, chatbots, and language model-based solutions.
RAG Systems
Review of architectures connected to internal documentation, embeddings, search engines, and corporate repositories.
Agents and tool use
Analysis of connected tools, APIs, permissions, automated actions, and potential system abuses.
Guardrails and controls
Tests on limits, filters, internal policies and protection mechanisms against malicious entries.
Specific risks in AI systems
We evaluate the most relevant attack scenarios in solutions based on generative AI, LLMs, RAG systems and agents connected to corporate data, tools or processes.
Prompt direct injection
Tests aimed at manipulating the model's behavior through malicious instructions entered directly by the user.
Prompt indirect injection
Evaluation of attacks in which AI receives malicious instructions from documents, web pages, emails, RAG sources, or other external content.
Jailbreaks and filter evasion
Simulation of attempts to bypass restrictions, internal policies, security filters, or limits defined for system behavior.
Data leak and sensitive information
Tests to detect if the system can expose internal data, confidential information, system prompts, private context, or unauthorized documentation.
Attacks on RAG systems
Risk assessment in architectures that connect language models with document databases, search engines, embeddings, or corporate repositories.
Abuse of tools and function calling
Analysis of the misuse of tools connected to the model, including API calls, execution of actions, escalation of permissions, or unintended access to systems.
Offensive security applied to AI
We apply a specific methodology for Artificial Intelligence systems, combining technical recognition, attack simulation, impact analysis, and remediation recommendations.
Scope definition
Technical recognition
Offensive testing
Analysis of findings
Remediation and retest plan
Reduce exposure before scaling your AI
AI Pentesting helps detect technical, operational, and business risks before they affect users, customers, data, or critical processes.
Security before deployment
Identify risks before the AI system is exposed to internal users, customers, or third parties.
Reduction of information leaks
Detects potential avenues for exposure of sensitive data, prompts, credentials, or internal documentation.
Greater control over agents
Evaluate whether agents can perform actions outside their scope or access unauthorized tools.
Evidence for compliance
It provides useful information for auditing, AI governance, risk management, and internal controls.
Lower operational risk
It reduces the risk of inappropriate responses, system abuse, or unforeseen automated actions.
Brand protection and trust
Minimize incidents that could affect customers, employees, corporate reputation, or service continuity.
Do you want to assess the security of your AI solutions?
We help you identify real risks, prioritize actions, and strengthen your systems before scaling new use cases.
Evaluate the security of your AI systems
Tell us what solution you want to review and we'll help you define the scope of the penetration testing: architecture, model, data, connected tools, and level of exposure.
Let's talk about your project
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Offensive security for AI
Evaluate the actual security of your Artificial Intelligence systems
Access the content and discover how to identify vulnerabilities, abuse risks, information leaks, and specific attack scenarios in solutions based on generative AI, LLMs, and agents.
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