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Continuous & Automated Agentic AI Red Teaming

Red team AI systems and agents by emulating real attacker behavior to uncover high-impact vulnerabilities across models, tools, data, and workflows before they are exploited.

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AI Red Teaming Dashboard

Continuous & Automated AI Red Teaming

Red team AI systems and agents by emulating real attacker behavior to uncover high-impact vulnerabilities across models, tools, data, and workflows before they are exploited.

Book a Demo
Stress Test AI Against Real Attacker Behavior

By chaining domain-specific attacks across one-shot and multi-step interactions, Mindgard reveals where guardrails hold, degrade, and fail across AI workflows. Its attack techniques are continuously strengthened by insights from Mindgard’s vulnerability research and public disclosures.

Attacker-Aligned AI Red Teaming

Mindgard automates AI red teaming by emulating real adversary workflows, including reconnaissance, exploitation planning, and execution, to reveal how attackers can misuse AI systems to achieve real objectives.

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Using Burp for AI Red Teaming

System-Level AI Security

Mindgard secures complete AI systems rather than isolated models, capturing how agents, tools, APIs, data sources, and workflows interact to expose vulnerabilities that only emerge at the system level.

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Attack Simulation

By chaining domain-specific attacks across one-shot and multi-step interactions, Mindgard reveals where guardrails hold, degrade, and fail across AI workflows. Its attack techniques are continuously strengthened by insights from Mindgard’s vulnerability research and public disclosures.

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MITRE Atlas Adviser

Actionable Findings and Remediation Guidance

Mindgard surfaces high-impact vulnerabilities with clear evidence, attacker context, and actionable remediation guidance, enabling security teams to prioritize fixes, validate defenses, and reduce AI risk without operational disruption. Identified risks are mapped to global and industry frameworks, including the EU AI Act, NIST AI Risk Management Framework, OWASP LLM Top 10, and MITRE ATLAS, allowing organizations to translate technical findings into defensible governance, compliance, and reporting outcomes.

Continuous AI Risk Discovery and Assessment

Mindgard continuously red teams AI systems as they evolve, identifying new attack paths, behavioral weaknesses, and exploit opportunities introduced by model updates, configuration changes, or expanded capabilities.

AI Risk Discovery

Most Popular Resources

Whether you're just getting started with AI Security or looking to deepen your expertise, our engaging content is here to support you every step of the way.

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What happens when you turn an AI model’s safety controls off?

Explore how model abliteration removes AI safety controls, the legitimate security applications it enables and the risks unrestricted models create for cyberattacks, evaluation and regulation.

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kNNGuard: A Training-Free Guardrail for Adaptive AI Defense

New Mindgard and Lancaster University research introduces kNNGuard, a training-free guardrail that uses LLM hidden activations to detect unsafe, off-topic and adversarial prompts without fine-tuning.

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Behind the Refusal: How Black-Box Guardrail Reconnaissance Works

New research introduces a black-box method to detect and profile hidden AI guardrails as well as distinguish their blocks from LLM safety refusal.

How does Mindgard automate AI red teaming?
Mindgard uses attacker-style reconnaissance, proprietary vulnerability intelligence, and adaptive attack agents to identify and validate high-impact vulnerabilities across AI systems and agents. Its offensive security platform emulates adversary workflows from reconnaissance through exploitation, then provides evidence, attacker context, and remediation guidance. Mindgard reduces AI risk assessments from weeks to hours and continuously verifies defensive gaps and policy compliance as AI systems change.
How much does AI red teaming cost?
The cost of AI red teaming depends on the number and complexity of systems, the assessment scope, the techniques required, and whether testing is manual or automated. Manual engagements can require weeks of specialist work and are usually priced per project. Automated AI red teaming platforms reduce repeated manual effort and support continuous assessment across releases. Mindgard pricing is based on each organization’s environment and requirements.
How does AI red teaming support governance and compliance?
AI red teaming produces evidence of how systems were assessed, which vulnerabilities were found, whether they were exploitable, and how teams responded. Findings can be mapped to frameworks such as the OWASP Top 10 for LLM Applications, MITRE ATLAS, NIST AI Risk Management Framework, and relevant EU AI Act requirements. This helps organizations demonstrate continuous policy compliance and provide defensible reporting to stakeholders and auditors.
Do AI guardrails replace AI red teaming?
No. Guardrails apply policies to AI inputs, outputs, or actions, while AI red teaming determines whether those controls can be bypassed. Red teaming reveals where guardrails hold, degrade, or fail under adaptive attacks and multi-step interactions. Organizations need both: guardrails to enforce policies and continuous AI red teaming to validate that those policies and defenses remain effective.
How often should AI red teaming be performed?
AI red teaming should be performed before deployment and continuously in production. AI systems can change when teams update a model, system prompt, dataset, guardrail, tool, permission, or integration. Each change can create a new attack path or weaken an existing control. Continuous AI red teaming helps teams find emerging vulnerabilities and verify that remediation remains effective as the system evolves.
What is the difference between manual and automated AI red teaming?
Manual AI red teaming relies on human experts to develop creative attacks and investigate complex behavior. Automated AI red teaming executes adversarial techniques faster, more consistently, and at greater scale. Manual expertise remains valuable for novel and highly contextual scenarios, while automation enables continuous assessment. The strongest approach combines research-led attack intelligence with automation that can adapt and retest systems as they change.
How is AI red teaming different from AI model evaluation?
AI model evaluation measures characteristics such as accuracy, quality, reliability, bias, or performance against a defined dataset or benchmark. AI red teaming adopts an adversarial perspective and actively attempts to make an AI system violate its security, safety, or policy requirements. It focuses on finding exploitable attack paths and defensive gaps rather than producing a general measure of model performance.
How is AI red teaming different from traditional penetration testing?
Traditional penetration testing primarily looks for vulnerabilities in software, networks, infrastructure, APIs, and configurations. AI red teaming examines how AI behavior can be manipulated through prompts, context, data, memory, models, tools, and multi-step interactions. The two practices complement each other: penetration testing secures the conventional technology stack, while AI red teaming addresses the behavioral and system-level risks introduced by AI.
What types of AI systems can be red teamed?
AI red teaming can assess large language models, generative AI applications, chatbots, copilots, RAG systems, multimodal models, autonomous agents, agentic workflows, and systems connected to external tools or APIs. System-level AI red teaming also evaluates components such as prompts, memory, data sources, guardrails, MCP or A2A servers, permissions, and infrastructure rather than examining the underlying model in isolation.
What vulnerabilities can AI red teaming identify?
AI red teaming can identify prompt injection, jailbreaks, sensitive information disclosure, system prompt leakage, model extraction, unsafe tool use, excessive agency, data or memory poisoning, and insecure output handling. It can also uncover weaknesses that only become exploitable when models, agents, data, APIs, permissions, and connected tools interact as part of a complete AI system.
What is agentic AI red teaming?
Agentic AI red teaming uses autonomous attack agents to perform reconnaissance, plan attack paths, adapt techniques, and chain multiple actions together. It goes beyond running a fixed collection of prompts. Agentic AI red teaming emulates how an attacker explores a target, learns from its responses, and changes tactics to bypass controls or achieve a specific objective.
What is automated AI red teaming?
Automated AI red teaming uses software and autonomous agents to generate, execute, and evaluate adversarial attacks at scale. Unlike a point-in-time manual assessment, automated AI red teaming can run repeatedly as models, prompts, tools, guardrails, and configurations change. It helps organizations expand attack coverage, shorten assessment time, and identify new vulnerabilities without depending entirely on scarce AI security specialists.
What is AI red teaming?
AI red teaming is an adversarial security assessment that identifies how attackers could manipulate or exploit AI systems. It examines models, applications, agents, prompts, data, tools, APIs, and workflows to uncover vulnerabilities and defensive gaps. Effective AI red teaming provides evidence of which weaknesses are exploitable, their potential impact, and how security and engineering teams can close them.
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Start Securing Your AI Systems

See how Mindgard exposes and fixes exploitable AI risk across your AI agents and systems.

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Mindgard helps organizations discover, assess and defend their AI systems. Spun out of more than a decade of AI security research at Lancaster University in the UK and headquartered in Boston and London, Mindgard operationalizes the expertise of AI researchers and offensive security practitioners through a Security Platform that performs Shadow AI discovery, AI red teaming, and run-time AI protection to assess and mitigate risk across models, agents, and applications.

AICPA SOC SOC 2 Type 2 Compliant