🤖AI-Driven • End-to-End Agentic • No Human in Loop

Voice AI Red Teaming

Business-risk specific penetration testing for voice AI agents. Autonomous adversarial simulations that uncover vulnerabilities human testers can't find.

Aligned with OWASP LLM Top 10 and NIST AI RMF frameworks

🎯 Schedule Demo▶️ Watch Attack Demo

End-to-End Agentic Voice AI Security

Unlike traditional pentesting, Audn Red Voice operates autonomously with no human in the loop. Our AI adversary conducts millions of attack simulations tailored to your specific business risks.

🧠

AI-Driven Attack Generation

Pingu Unchained LLM generates novel attack vectors specific to your voice agent's business context and industry.

🔄

Continuous Red Teaming

Not a one-time audit. Continuous adversarial testing as your voice agent evolves and threats emerge.

📊

Business Risk Scoring

Every vulnerability mapped to specific business impact: financial loss, compliance violation, reputation damage.

Real-Time Attack Simulation

Live voice calls testing your production systems under realistic attack conditions.

🎭

Multi-Persona Attacks

Simulates diverse attacker profiles, from script kiddies to sophisticated nation-state actors.

📋

Compliance Evidence

Automated documentation for SOC 2, ISO 27001, NIST AI RMF, and EU AI Act requirements.

Voice-Specific Attack Vectors

Voice AI introduces unique attack surfaces that text-based security tools miss entirely.

🎭

Social Engineering

Psychological manipulation techniques adapted for voice interfaces.

🔊

Voice Cloning

Deepfake audio attacks bypassing voice biometric systems.

💉

Prompt Injection

Injecting malicious instructions through conversational prompts.

📤

Data Exfiltration

Extracting sensitive information through conversational techniques.

🔓

Jailbreaking

Bypassing safety guardrails and content policies.

🚫

Denial of Service

Disrupting voice agent availability and performance.

🛡️OWASP LLM Top 10 (2025)

Complete Coverage of OWASP LLM Vulnerabilities

Every attack category mapped to voice-specific vectors and tested autonomously.

LLM01Critical

Prompt Injection

Manipulating LLM behavior through crafted inputs that override system instructions.

Voice Attack Vector:

Voice-based prompt injection using tone, pacing, and emotional manipulation.

LLM02High

Insecure Output Handling

Failing to validate LLM outputs before passing to downstream systems.

Voice Attack Vector:

Voice agents executing unauthorized actions based on manipulated responses.

LLM03High

Training Data Poisoning

Corrupting training data to influence model behavior.

Voice Attack Vector:

Voice pattern training corruption leading to authentication bypass.

LLM04Medium

Model Denial of Service

Resource exhaustion attacks targeting LLM infrastructure.

Voice Attack Vector:

Voice flood attacks causing agent unavailability or degraded service.

LLM05High

Supply Chain Vulnerabilities

Compromised components in the LLM pipeline.

Voice Attack Vector:

Compromised voice synthesis or STT components injecting malicious content.

LLM06Critical

Sensitive Information Disclosure

LLM revealing confidential data through responses.

Voice Attack Vector:

Voice agents leaking PII, credentials, or business secrets via conversation.

LLM07High

Insecure Plugin Design

Vulnerable integrations expanding the attack surface.

Voice Attack Vector:

Voice-triggered tool calls executing unauthorized operations.

LLM08Critical

Excessive Agency

LLM taking actions beyond intended scope.

Voice Attack Vector:

Voice agents performing unauthorized transactions or data access.

LLM09High

Overreliance

Trusting LLM outputs without verification.

Voice Attack Vector:

Voice authentication bypass through deepfake or voice cloning.

LLM10Medium

Model Theft

Extracting proprietary model information.

Voice Attack Vector:

Voice-based model extraction revealing system prompts and configurations.

🏛️NIST AI Risk Management Framework

Aligned with NIST AI RMF

Our testing methodology maps directly to NIST AI RMF core functions.

Govern

Establish AI risk management policies and accountability structures.

Automated compliance evidence generation for voice AI deployments.

Map

Identify and document AI system contexts and potential impacts.

Continuous attack surface mapping for voice agent interfaces.

Measure

Analyze and assess AI risks using appropriate metrics.

Quantitative risk scoring based on real-world attack simulations.

Manage

Prioritize and act on AI risks based on projected impact.

Automated remediation guidance and defense hardening.

How Audn Red Voice Works

01

Connect Your Voice Agent

Provide phone number or API endpoint. We integrate with Twilio, Genesys, Amazon Connect, or any custom platform.

02

Define Business Risks

Specify what matters: unauthorized transactions, data leakage, compliance violations, reputation risks. We test what impacts your business.

03

Autonomous Attack Campaign

Pingu Unchained launches thousands of AI-generated attack scenarios. No scripts. No human testers. Pure adversarial AI.

04

Real-Time Analysis

Every call analyzed for vulnerabilities, successful attacks, and near-misses. Emotion analysis detects manipulation susceptibility.

05

Risk Report & Remediation

Detailed findings mapped to OWASP/NIST frameworks with prioritized remediation guidance and compliance evidence.

Get Your Voice AI Security Assessment

Schedule a demo or request a penetration test for your voice AI agent. Our team will contact you within 24 hours.