Solution

AI - Infused
Security Assessment

GenAI security. Practical assurance.

Specialized security testing for AI-powered applications — chatbots, copilots, RAG systems, autonomous agents, and GenAI platforms.

Security for the
AI attack surface

AI - Infused Security Assessment is a specialized security assessment designed to identify vulnerabilities, weaknesses, and abuse cases in AI-powered applications, chatbots, copilots, RAG systems, autonomous agents, and GenAI platforms. The objective is to determine whether an attacker can manipulate the model, access unauthorized data, bypass safeguards, execute unintended actions, or impact the confidentiality, integrity, and availability of the AI system.

Ownzap tests every layer of your AI stack — from prompts and retrieval to agents and APIs — so you can validate security before attackers exploit the gaps.

GenAI Platform Models · Context · Actions

CIA

confidentiality, integrity & availability

  • Confidentiality
  • Integrity
  • Availability
  • Abuse cases

Testing domains

prompt, leakage, RAG and agents

  • Prompt injection
  • Data leakage
  • RAG security
  • Agentic AI

GenAI

chatbots, copilots, agents & RAG

  • Chatbots
  • Copilots
  • Autonomous agents
  • RAG systems

Our AI - Infused security assessment

Five focused testing domains to uncover how attackers can exploit your AI stack — from adversarial prompts to autonomous tool chains.

Prompt Injection Testing

Evaluate whether adversarial inputs can override system instructions, bypass guardrails, or hijack model behaviour through direct and indirect injection vectors.

  • Direct and indirect prompt injection attempts
  • Jailbreak and instruction-override scenarios
  • System prompt and policy bypass testing
  • Multi-turn conversation manipulation
Illustration of prompt injection attack path into an AI model

Data Leakage Assessment

Identify whether the model or context layer exposes sensitive training data, internal documents, credentials, or personal information through responses.

  • PII and credential exposure via model outputs
  • Context window and memory leakage
  • Cross-session and cross-user data bleed
  • Training data extraction attempts
Illustration of sensitive data leaking from an AI system

RAG (Retrieval-Augmented Generation) Security Testing

Test retrieval pipelines, vector stores, and document grounding for poisoning, unauthorized access, and manipulation of generated answers.

  • Vector store and document access controls
  • Retrieval poisoning and grounding attacks
  • Cross-tenant and scope-boundary bypass
  • Embedding and chunk-level data exposure
Illustration of RAG retrieval and grounding security

Agentic AI Security Testing

Assess autonomous agents that invoke tools, APIs, and workflows — validating whether attackers can trigger unintended actions or escalate privileges.

  • Tool invocation and action-chain abuse
  • Privilege escalation via agent permissions
  • Unsafe autonomous decision paths
  • Human-in-the-loop bypass scenarios
Illustration of agentic AI tool and action chain security

API Security Testing

Review model endpoints, inference gateways, and integration layers for authentication gaps, authorization flaws, and abuse of AI service APIs.

  • Model and inference endpoint hardening
  • Authentication, authorization, and rate limiting
  • Token and key management for AI services
  • Input validation and output filtering at the API layer
Illustration of AI API and gateway security testing

Next step

Secure your AI systems before attackers do

Talk with Ownzap about assessing prompt injection, data leakage, RAG pipelines, agentic workflows, and AI API security for your platform.

Contact us