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ZIVIS RT

Adversarial AI Security Testing

Test your LLM-powered applications against real-world attack scenarios. Our red team agents simulate sophisticated adversaries to find prompt injection, jailbreaks, data exfiltration, and all OWASP LLM Top 10 vulnerabilities before attackers do.

View OWASP Coverage

Why Red Team Your AI?

The Threat Landscape

AI systems face unique attack vectors that traditional security testing doesn't cover. Prompt injection is now the #1 vulnerability in enterprise AI deployments.

  • Prompt injection attacks are easy to execute
  • Jailbreaks bypass content filters daily
  • Data exfiltration through AI is a real risk
  • Excessive agency can lead to unauthorized actions

Proactive Defense

Red teaming finds vulnerabilities before attackers do. Our automated agents simulate real-world adversaries at scale, testing thousands of attack variations.

  • Find vulnerabilities before production
  • Test at scale with automated agents
  • Get remediation guidance, not just findings
  • Prove security posture to stakeholders

OWASP LLM Top 10 Coverage

Complete testing coverage for all OWASP LLM Top 10 (2025) vulnerability categories

LLM01CRITICAL

Prompt Injection

Manipulating LLM behavior through crafted inputs

LLM02CRITICAL

Insecure Output Handling

Unsafe use of LLM outputs in downstream systems

LLM03HIGH

Training Data Poisoning

Manipulation of training data to compromise model behavior

LLM04HIGH

Model Denial of Service

Resource exhaustion attacks against LLM systems

LLM05HIGH

Supply Chain Vulnerabilities

Compromised dependencies and third-party models

LLM06CRITICAL

Sensitive Information Disclosure

Leaking confidential data through model outputs

LLM07HIGH

Insecure Plugin Design

Unsafe tool and plugin implementations

LLM08CRITICAL

Excessive Agency

LLM taking actions beyond intended scope

LLM09MEDIUM

Overreliance

Blind trust in LLM outputs without validation

LLM10HIGH

Model Theft

Extraction of proprietary model weights and behaviors

How Campaigns Work

Automated red team agents execute thousands of attack variations against your AI systems

1

Configure Target

Define your AI system endpoints, authentication, and scope

2

Select Methodology

Choose structured, adversarial, or hybrid testing approach

3

Launch Agents

Our red team agents execute attacks at scale

4

Review Findings

Get prioritized findings with remediation guidance

What We Can Test

Chat Interfaces

Customer-facing chatbots and conversational AI

APIs

LLM-powered APIs and backend services

Agent Systems

Autonomous AI with tool access and actions

RAG Applications

Retrieval-augmented generation systems

Testing Methodologies

Choose the approach that fits your security requirements

Structured Testing

Systematic coverage of all OWASP LLM Top 10 categories with documented test cases and reproducible findings.

  • Full OWASP coverage
  • Documented test cases
  • Reproducible results
  • Compliance-ready reports

Adversarial Testing

Simulated attacker behavior using creative, evolving attack chains that mirror real-world threat actors.

  • Attack chain simulation
  • Evolving techniques
  • Persistence testing
  • Real-world scenarios

Hybrid Approach

Combines structured coverage with adversarial creativity for comprehensive security validation.

  • Best of both methods
  • Maximum coverage
  • Creative exploitation
  • Thorough documentation

Testing Frequency

From point-in-time assessments to continuous security testing

One-Time

Point-in-time assessment for pre-launch validation

Quarterly

Regular assessments aligned with release cycles

Weekly

Rapid iteration testing for agile development

Continuous

Ongoing testing integrated into CI/CD pipelines

Use Cases

Pre-Production Validation

Test your AI system before launch to find and fix vulnerabilities before attackers do.

Ongoing Security Testing

Continuous testing for deployed AI systems to catch vulnerabilities introduced by updates.

Compliance Evidence

Generate evidence for security controls required by ISO 42001, SOC 2, and other frameworks.

Vendor Evaluation

Assess third-party AI systems before integration to understand security risks.

Flows Into ZIVIS Trust

Red team findings automatically map to trust assessment controls, providing evidence for your security posture without manual data entry.

ZIVIS RT
Findings auto-map to
Security Controls
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Find Vulnerabilities Before Attackers Do

Start red teaming your AI systems today. Our agents test thousands of attack variations so you don't have to.

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