AI Red Teaming Services for LLM and Agentic AI Vulnerability Assessment

AI red teaming is a structured adversarial testing process where security experts simulate real-world attacks against AI systems—such as LLMs or agentic AI—to identify exploitable vulnerabilities. This includes prompt injection, jailbreaking, model extraction, data poisoning, and hallucination manipulation. Unlike traditional penetration testing, AI red teaming requires specialized knowledge of model behavior, training data risks, and inference-time attack surfaces.

AI red teaming typically uncovers prompt injection attacks, indirect prompt injections via external data sources, jailbreaks that bypass safety guardrails, model inversion and extraction attacks, training data poisoning, insecure tool use in agentic workflows, excessive agency exploitation, and output manipulation. These vulnerabilities are unique to AI systems and require purpose-built adversarial testing methodologies to surface effectively.

Traditional penetration testing targets network, application, and infrastructure layers using established exploit techniques. AI red teaming specifically targets the behavior, reasoning, and output of machine learning models and LLMs. It involves crafting adversarial inputs, manipulating context windows, testing multi-agent trust boundaries, and assessing model-specific risks that conventional security tools are not equipped to detect.

Protectt.ai's AI Red Teaming covers the full AI lifecycle—from model development and supply chain security (via ML Model Scanner) through runtime production monitoring (via LLM Runtime Security). Engagements assess LLM deployments, agentic AI workflows, API integrations, and tool-use chains. Testing is customized to your AI stack, risk appetite, and applicable regulatory requirements such as ISO 42001.

Protectt.ai supports red teaming for a wide range of AI systems including proprietary LLMs, open-source models, fine-tuned enterprise models, and multi-agent agentic AI architectures. Whether your AI is deployed on cloud infrastructure, on-premise, or as a hybrid setup, our platform is designed to assess vulnerabilities across diverse AI deployment configurations at enterprise scale.

Yes. Our red teaming assessments are aligned with ISO 42001 (AI Management Systems), ISO 27001, and emerging AI governance frameworks. Findings are mapped to compliance gaps, and remediation guidance is provided to help your organization meet regulatory obligations, reduce audit preparation time, and proactively manage AI-related legal and reputational risks across global jurisdictions.

Assessment duration depends on the complexity and scope of your AI deployment. A focused LLM vulnerability assessment for a single model typically takes one to two weeks. Comprehensive agentic AI lifecycle assessments covering multiple models, tool integrations, and runtime environments may take three to six weeks. Protectt.ai's automated adversarial testing capabilities help accelerate timelines without sacrificing depth of coverage.

At the conclusion of an engagement, Protectt.ai provides a detailed vulnerability report cataloguing all identified AI-specific risks with severity ratings, attack scenario descriptions, and evidence. This is accompanied by a prioritized remediation roadmap, compliance gap analysis, and executive summary. Ongoing runtime protection and re-testing options are also available to validate that remediations have been effectively implemented.