Mindgard - Automated AI red teaming and security testing from attacker perspective

What is Mindgard

As organizations rapidly deploy AI systems across financial services, healthcare, manufacturing, and enterprise operations, a new security reality has emerged: traditional security tools simply cannot see the threats targeting AI. The攻击者 are actively exploiting AI-specific vulnerabilities—prompt injections, agent misconfigurations, behavioral manipulations, and model bypasses—that conventional AppSec platforms were never designed to detect. This gap has created an urgent need for security approaches that think like attackers.

Mindgard positions itself as the first automated AI red teaming and security testing platform built from an attacker's perspective. Rather than treating AI security as an afterthought or relying on generic vulnerability scanning, Mindgard simulates how real adversaries think and operate when targeting AI systems. The platform continuously tests your AI deployments against thousands of realistic attack scenarios, identifying weaknesses before malicious actors can exploit them.

The company's track record speaks to its effectiveness: Mindgard has already disclosed over 70 real AI security vulnerabilities affecting major technology platforms, including Google Antigravity IDE, OpenAI Sora, and Zed IDE. This isn't theoretical research—these are confirmed vulnerabilities that have been responsibly disclosed and patched. The platform serves thousands of global users, ranging from the world's largest software procurement organizations to fast-growing AI-native companies. Industry recognition has followed, including SC Awards Europe 2025 winning both "Best AI Solution" and "Best New Company," plus coverage from S&P Global.

Mindgard's Core Capabilities

Mindgard provides a comprehensive suite of AI security testing capabilities designed to protect your AI deployments throughout their lifecycle. Each feature delivers tangible value for security teams struggling to gain visibility into their AI attack surface.

AI Discovery & Assessment enables you to map your entire AI attack surface. Many organizations have deployed AI systems they don't even know about—shadow AI that emerged from individual team initiatives or rapid prototyping. Mindgard identifies these unknown systems by enumerating behaviors, integrations, and access paths, giving you a complete inventory of AI assets and their associated risks.

Automated AI Red Teaming brings attacker-aligned testing to your security program. Rather than checking against predefined vulnerability lists, Mindgard simulates how sophisticated adversaries approach AI systems. The platform conducts continuous security assessments, automatically discovering vulnerabilities that traditional testing would miss. This isn't a point-in-time scan—it's ongoing protection that adapts as your AI systems evolve.

Offensive Security Testing takes deep penetration testing capabilities and applies them specifically to AI systems. Mindgard's red team simulates real attacker behavior, probing for weaknesses in how your AI models interact with external systems, process user inputs, and execute autonomous actions. The platform documents findings with actionable risk analysis that integrates into your existing security workflows.

Model Scanning examines AI models and artifacts before deployment. Just as you would scan code for vulnerabilities before releasing software, Mindgard scans your trained models to identify model-level vulnerabilities that could be exploited in production. This pre-deployment gate ensures compromised or risky models never reach your users.

Emerging Threats Monitoring keeps pace with the rapidly evolving AI threat landscape. As developers introduce new integrations, tools, or data sources, Mindgard automatically tests for newly discovered attack vectors. The platform maintains real-time threat intelligence specifically focused on AI risks, ensuring you're protected against the latest techniques attackers are using in the wild.

AI Guardrail Testing evaluates whether your deployed guardrails and WAF solutions are actually working. Many organizations have invested in AI protection tools but lack any way to verify their effectiveness. Mindgard stress-tests these defenses, identifying gaps before attackers discover them.

Model Risk Comparison addresses a critical gap for organizations using fine-tuned models. When you customize a base model with proprietary data, you may inadvertently introduce new security risks. Mindgard benchmarks your custom models against baseline models, highlighting specific areas where fine-tuning has created new attack surfaces.

Scalable Red Teaming empowers your existing penetration testing team to conduct AI security assessments efficiently. Rather than requiring specialized AI security expertise, Mindgard provides the automation and guidance your team needs to expand their capabilities into AI systems.

Who Is Using Mindgard

Mindgard addresses real security challenges faced by organizations across industries. These scenarios represent where the platform delivers the most immediate value.

Shadow AI Discovery is often the first wake-up call for security leaders. Imagine you lead security at a mid-sized financial services firm. Over the past year, various teams have spun up AI tools for customer service chatbots, document processing, fraud detection, and predictive analytics. Most of these were deployed quickly to address urgent business needs. Now you need to answer a basic question: what AI systems do we actually have, and which ones are exposing us to risk? Mindgard discovers these systems by analyzing behavior patterns, integration points, and access pathways—even those that were never formally documented or approved. The output isn't just a list; it's a risk-ranked inventory that tells you which systems need immediate attention.

System Prompt Security represents a hidden attack vector most organizations don't understand. Your AI system's prompt is essentially its instruction manual—it defines what the AI can do, what it should refuse, and how it should behave. Attackers have developed sophisticated techniques to override, bypass, or hijack prompts. Mindgard simulates these attacks, testing whether your prompts can be forced, manipulated, or circumvented. The platform identifies prompt injection weaknesses, guardrail gaps, and unsafe tool interactions that could allow attackers to make your AI do things it shouldn't.

Production AI Security addresses a fundamental truth: AI behaves differently in production than in development. Training and testing environments don't capture the full complexity of real-world interactions—unexpected user inputs, novel attack techniques, and emergent behaviors only appear after deployment. Mindgard continuously tests your production AI systems, identifying risks that emerge in real-world conditions. This ongoing vigilance catches vulnerabilities that would otherwise go unnoticed until a breach occurs.

Coverage Beyond Traditional AppSec matters because conventional security tools assume deterministic behavior. Traditional AppSec scanners look for known vulnerability patterns—SQL injection, cross-site scripting, buffer overflows. These approaches can't see AI-specific threats because AI systems are probabilistic, adaptive, and often autonomous. Risks only appear at runtime based on specific input combinations or interaction patterns. Mindgard uses attacker-aligned methodology specifically designed to discover prompt injections, agent misuses, behavioral manipulations, and other threats that exist only in the AI context.

CI/CD Integration brings security into your development workflow. Every time you update a model, modify a prompt, or add a new integration, you could be introducing new vulnerabilities. Mindgard provides GitHub Actions and CI/CD pipeline integrations that automatically run security tests with every change. This means your AI security testing happens at the same velocity as your development process—no more shipping untested AI changes to production.

Multi-Model Type Support ensures comprehensive coverage regardless of your AI architecture. Modern enterprises use diverse AI systems: large language models for text generation, NLP systems for sentiment analysis, computer vision models for image processing, audio models for speech recognition, and multimodal systems that combine multiple capabilities. Mindgard is neural network-agnostic, meaning it can test all of these model types with the same rigorous methodology.

Technical Foundation and Security Compliance

Mindgard's technical foundation reflects deep expertise in AI security research. The platform originated from Lancaster University's AI security research group, established in 2016 and recognized as the world's largest academic AI security laboratory. This connection provides Mindgard with a research pipeline that keeps the platform ahead of emerging threats—vulnerabilities discovered in academic research become attack scenarios in the platform within weeks.

The research team driving Mindgard's development includes domain experts with extensive industry experience. CEO James Brear brings enterprise leadership, while Chief Scientific Officer Dr. Peter Garraghan contributes over a decade of AI security research as the company's founder. The offensive security team is led by Rich Smith, and the research and innovation function is headed by Aaron Portnoy—bringing real-world penetration testing expertise to AI security. This combination of academic rigor and practical security experience is rare in the AI security space.

The platform's attack library contains thousands of unique AI attack scenarios, each developed through a combination of published research, real-world vulnerability disclosures, and continuous red team exercises. These aren't generic security tests adapted for AI—they're purpose-built for the unique attack surface that AI systems present. The configuration process is streamlined: users report getting their first tests running within 5 minutes of account creation.

Security and compliance are foundational commitments. Mindgard has achieved SOC 2 Type II compliance, demonstrating rigorous controls over data security, availability, and privacy. The platform is also GDPR compliant, with appropriate data processing agreements and geographic data handling options. Looking ahead, Mindgard is pursuing ISO 27001 certification, with audit completion expected in early 2026. These certifications provide enterprise security teams with the assurance they need when evaluating AI security platforms.

Frequently Asked Questions