AI Red Team Services for LLM & AI Systems
Our AI Red Team Services identify and exploit vulnerabilities in generative AI, LLM applications, and AI-powered systems before attackers do.
Get A Quote
Request a quote for our AI Red Team Services. Our team will review your AI environment and respond within 24 hours to discuss scope, objectives, and next steps.
What Is AI Red Teaming?
AI red team services simulate real-world attacks against artificial intelligence systems to uncover vulnerabilities before they can be exploited. Unlike traditional red teaming, which targets networks and infrastructure, AI red teaming focuses on the unique risks of machine learning models, LLMs, generative AI systems, and AI agents. It evaluates how AI behaves under adversarial conditions such as prompt injection, model manipulation, data poisoning, and policy bypass attempts. Because AI systems can be deceived without a traditional software flaw, adversarial AI testing is critical to ensuring secure and resilient AI deployments.
Our AI red teaming engagements focus on:
LLM Security Testing
Testing large language models for prompt injection and jailbreak vulnerabilities.
Guardrail Bypass Testing
Attempting to manipulate model outputs to override safety controls and policies.
Data Leakage Analysis
Identifying sensitive data exposure risks within generative AI systems.
AI Agent Exploitation
Assessing AI agents with excessive permissions for misuse and privilege abuse.
Adversarial Simulation
Simulating real-world attacker interactions to evaluate model resilience under pressure.
Why Traditional Security Testing Is Not Enough for AI
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Prompt Injection Attacks
AI systems can be manipulated through crafted inputs that override instructions and bypass built-in safeguards, even when no software vulnerability exists.
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Model Jailbreaks
Attackers can coerce large language models into ignoring safety policies, generating restricted content, or performing unintended actions.
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Data Poisoning
Compromised training data or manipulated inputs can influence model behavior, leading to biased, insecure, or harmful outputs.
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Model Inversion
Threat actors may extract sensitive training data or reconstruct private information directly from model responses.
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AI Supply Chain Risks
Third-party models, plugins, APIs, and external data sources introduce hidden risks that traditional testing often overlooks.
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AI Agent Exploitation
Autonomous AI agents with excessive permissions can be manipulated to execute unauthorized actions, access sensitive systems, or escalate privileges.
Certified for Excellence
Industry-Recognized Certifications
Our AI Red Team Methodology
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1. Scoping & AI Threat Modeling
We define engagement objectives, map the AI architecture, identify trust boundaries, and determine realistic attacker scenarios based on your specific AI use cases.
2. Adversarial Attack Simulation
We execute controlled prompt injection, jailbreak, model evasion, and data manipulation attacks to simulate how real-world adversaries would target your AI systems.
3. Exploitation & Impact Validation
We validate discovered weaknesses by demonstrating practical exploitation paths and measuring potential business, security, and operational impact.
4. Reporting & Remediation Strategy
We deliver a detailed technical report, executive risk summary, and prioritized remediation roadmap aligned to security and compliance requirements.
AI Systems We Red Team
We conduct AI red teaming across a wide range of AI-powered systems to identify real-world security weaknesses before attackers do, including:
Large Language Models (LLMs)
AI chatbots
AI copilots
Autonomous AI agents
Machine learning models in production
AI-driven automation systems
Retrieval-Augmented Generation (RAG) systems
Common AI Security Gaps We Discover
Prompt injection vulnerabilities
Insecure model deployment
Over-permissive AI agents
Data leakage risks
Model misuse scenarios
Policy bypass mechanisms
What You Receive After an AI Red Team Engagement
Our AI red team engagement provides clear, actionable insights that translate technical findings into measurable business risk and prioritized remediation steps.
Executive risk summary
Technical vulnerability report
Exploitation proof-of-concept
Remediation roadmap
AI governance alignment recommendations
Why Choose Us
Why Choose Secure Wave Advisors
Practical AI Pentest Associate (PAPA) Certified
Our founder holds the Practical AI Pentest Associate (PAPA) certification, demonstrating validated expertise in AI penetration testing and adversarial model assessment.
Hands-On AI Penetration Testing Experience
We apply real-world attack techniques against LLMs and AI systems, going beyond theory to identify exploitable weaknesses.
Specialized Focus on AI Security
Unlike general cybersecurity firms, we concentrate specifically on AI red teaming, LLM security testing, and adversarial AI risk.
Beyond Traditional Cybersecurity
We understand that AI systems introduce new attack surfaces that require dedicated methodologies, not just conventional security testing approaches.
Get Started With Your AI Red Team Engagement
Secure your AI systems before adversaries exploit them. Our expert-led AI red teaming identifies prompt injection risks, model vulnerabilities, and real-world attack paths to strengthen your AI security posture and support compliance readiness.
Guarding Your Data, Securing Your Future.
FAQs
Traditional penetration testing focuses on networks, applications, and infrastructure, while AI red teaming specifically tests model behavior, prompt injection risks, jailbreak vulnerabilities, data leakage, and adversarial manipulation of AI systems.
We red team LLMs, AI chatbots, AI agents, ML models in production, AI-driven automation systems, and retrieval-augmented generation (RAG) environments.
Engagement timelines vary based on system complexity, but most AI red team assessments range from one to three weeks, including testing, validation, and reporting.
Emerging regulations and frameworks such as NIST AI RMF and the EU AI Act emphasize AI risk management, and red teaming helps demonstrate proactive security validation and governance readiness.
