Scan every AI artifact before it ships.
Third-party models and artifacts carry risks a virus scan won’t catch. Book a demo and see how Mindgard scans AI artifacts and attacks deployed systems — showing exactly what’s exploitable, mapped to OWASP & MITRE.
Live platform demo · real attack techniques · no slidewareThree assumptions that get AI deployments breached
Every one of these was said by a team we later found exploitable findings in.
Guardrails filter inputs they’ve seen before. Mindgard extracted Grok’s system prompt with soft elicitation — no known-bad strings required — after which the model offered dangerous guidance.
Traditional AppSec testing never touches AI-specific failure modes: prompt injection, jailbreak chains, model extraction, data poisoning. That’s why our research at Lancaster University led to founding Mindgard.
AI systems fail silently. A leaked system prompt or poisoned classifier throws no exception and trips no alert — until it’s public. We’ve publicly identified 150+ of these across leading AI systems.
The uncomfortable part: none of these teams were careless. They just had no way to see their AI the way an attacker does.
The only way to know if your AI is secure is to attack it the way an attacker would — continuously.
Not a questionnaire. Not a policy review. Real adversarial attacks, run against your real systems. Here’s what happens when we do that to the world’s leading AI:
Google Antigravity
Identified a flaw in Google’s Antigravity IDE showing how AI-driven software breaks traditional trust assumptions.
OpenAI Sora
Chained cross-modal prompts to surface hidden system instructions from OpenAI’s video generator.
xAI Grok
Extracted Grok’s system prompt using soft elicitation, after which the model offered dangerous guidance.
Zed IDE
Identified two vulnerabilities in the Zed IDE and ran a coordinated remediation process with its developers.
One continuous loop, attacker to defender
Mindgard profiles your AI the way an adversary does — then closes the gaps it finds.
Discover
Find every model, agent and shadow AI across your stack.
Recon
Map the attack surface and profile each system’s behaviour.
Attack
Run automated adversarial attacks at scale to surface real risk.
Defend
Get prioritised fixes and runtime protection, in your workflow.
A demo that attacks, not slides
No slideware. We’ll show you how Mindgard attacks AI systems like yours — prompt injection, jailbreaks, leakage, tool abuse — and walk through the exploitable findings it surfaces.
Security leaders and AppSec/ML owners with LLMs, agents, copilots or ML models in production — or about to be. If your AI only exists on a roadmap, the free analyst report at the bottom of this page is a better first step.
The worst case: you spend one call and never talk to us again. No obligation, no drip sequence you didn’t ask for. That’s the whole risk.
Book a Demo →What you’ll see in the demo
DEMOTrusted by enterprise security teams
How global organisations use Mindgard to find exploitable AI risk and act on it.
Reduced AI security testing cycles from weeks to hours across nine production AI models.
Cut through the noise to expose the vulnerabilities that truly mattered.
Uncovered AI-specific security risks and strengthened our posture faster.
Every week your AI goes untested is a week of unknown exposure.
The attacks in the console above aren’t hypothetical — they’re the same classes of attack behind the 150+ AI vulnerabilities we’ve publicly identified. The only variable is who runs them against your AI first.
Book a Demo →What security teams ask first
Is the demo a sales pitch?
No. It’s a working session: we run real attacks and you watch the findings surface. If it’s a fit, we’ll talk next steps. If not, you still leave knowing more about your AI risk than you did before the call.
How is this different from AI guardrails?
Guardrails filter known inputs at runtime. Mindgard acts as an autonomous red teamer, proactively discovering novel, exploitable vulnerabilities across the whole system before attackers do.
What does it actually test?
Prompt injection, jailbreaks, data leakage, model extraction, evasion and poisoning — across LLMs, GenAI, agents and ML models, with findings mapped to OWASP and MITRE.
How fast can we see value?
Deployment is designed to be fast — CI/CD, Burp Suite or a single click — so you can move from demo to testing your own systems without a heavy integration project.
Is it enterprise-ready?
Yes. SOC 2 Type 2 certified, built from a decade of Lancaster University research, headquartered in Boston and London, and integrated with SIEM and ticketing.
See your AI attack surface — live
Book a demo. See how Mindgard attacks AI systems like yours — and exactly what it finds.