Redline Cyber Security Launches Dedicated AI Pentesting Service

Artificial Intelligence is rapidly transforming enterprise applications. From copilots to customer-facing chatbots, AI and large language models (LLMs) are being embedded into critical workflows at an unprecedented pace. But with this innovation comes a new attack surface that traditional penetration testing alone cannot fully address.

At Redline Cyber Security, we’ve seen the shift firsthand. In recent penetration tests, we are increasingly encountering AI-enabled applications across industries. Customers began asking us to extend our adversarial tradecraft to these new systems, not only to validate the underlying applications but to expose the unique vulnerabilities that arise when AI is integrated.

Why Pentesting AI Matters

AI systems interpret inputs as instructions rather than inert data, which makes them susceptible to novel attack vectors. Common issues include:

• Prompt Injections – Hidden or malicious instructions overriding system rules.
• Jailbreaks and Filter Evasion – Circumventing safety policies and content restrictions.
• Context Window Exploits – Using scale to bury or override protective prompts.
• Emergent Behaviors – Creative or unintended outputs that leak sensitive data.
• Ecosystem Risks – Compromises via agents, RAG pipelines, plugins, APIs, and integrations.
• Malicious Outputs – AI-generated SQL queries, code, or scripts that act as payloads.

These vulnerabilities are not theoretical, they represent real risks that can cascade through an organization, leading to data exposure, privilege escalation, or systemic compromise.

Redline’s End-to-End AI Pentesting Approach

Our new service offering is built on the same principles that define all of Redline’s work: real adversaries, real testing, real results. We simulate attacker tradecraft across the entire AI ecosystem, including:

• System Input Mapping – Identifying all vectors where data or instructions reach the model.
• Ecosystem Attack Simulation – Probing infrastructure, micro-services, APIs, vector databases, and logging pipelines.
• Model Security Assessment – Testing for jailbreaks, safety bypasses, data poisoning, inversion, and bias amplification.
• Adversarial Prompt Exploitation – Context manipulation, hidden directives, and agent prompt poisoning.
• Data Integrity Analysis – Evaluating RAG pipelines, embeddings, and training data for poisoning or leakage.
• Application Security Testing – Identifying traditional vulnerabilities amplified by AI context, such as SSRF, XSS, IDOR, and insecure output handling.
• Exploitation Chaining and Lateral Movement – Demonstrating how attackers pivot from AI footholds into SaaS, cloud, or on-prem assets.

After combining decades of penetration testing expertise with insights from pioneers in the AI research community, our team has undergone extensive training directly from Jason Haddix over at Arcanum to deliver our customers a cutting-edge AI Pentesting service. This end-to-end methodology equips organizations with clarity on systemic risks, actionable remediation strategies, and the assurance needed to stay resilient against current and emerging threats.

Meeting Customer Demand

The launch of Redline’s AI Penetration Testing Service reflects the reality we are seeing across engagements: AI is no longer experimental, it is business-critical. Our clients want confidence that these systems can withstand real-world attacks. With this offering, we are delivering the clarity, confidence, and adversarial perspective required to secure AI-enabled applications.

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Ready to test your AI systems like real attackers would?

‍Contact Redline Cyber Security to schedule an AI Pentest and gain assurance over your LLM, agent, and RAG deployments.