Attackers are already red teaming your AI. Watch Mindgard get there first.
Book a demo of Mindgard’s automated AI red teaming platform. See how it emulates real attacker behavior against AI systems like yours and surfaces what is exploitable, with risks mapped to OWASP LLM Top 10 and MITRE ATLAS.
See the AI red teaming platform
Tell us what you are securing and we will schedule a walkthrough of how Mindgard finds exploitable risk in AI like yours.
Big AI. Bigger blind spots.
We found vulnerabilities in AI from OpenAI, Meta, Microsoft, NVIDIA, Google and Amazon, reported each one to the vendor, and disclosed it. 150+ AI vulnerabilities publicly identified, 30+ published disclosures, including these:
OpenAI ChatGPT
Bypassed ChatGPT’s image safeguards by using its memory feature to splice a more permissive system prompt into context, so it generated sexualised images of real and fictitious people it was built to refuse. Reported to OpenAI before publication.
Meta Prompt Guard
Evaded Meta’s Prompt Guard, the guardrail built to catch jailbreaks and prompt injection before they reach the model.
Microsoft Azure AI
Evaded Azure Prompt Shield and Azure AI Content Safety, Microsoft’s guardrails against prompt attacks and harmful content.
NVIDIA NemoGuard
Evaded NVIDIA’s NemoGuard jailbreak detection, the control meant to stop exactly this class of attack.
Google Antigravity
Identified a persistent code execution flaw in Google’s Antigravity IDE, showing how AI-driven software breaks traditional trust assumptions.
xAI Grok
Extracted Grok’s system prompt using soft elicitation, after which the model produced guidance it was built to refuse.
Each disclosure lists when it was reported to the vendor and when it was published. See the full disclosure list. The techniques uncovered feed straight back into the platform that red teams AI like yours.
AI red teaming, explained in 2 minutes 33 seconds
How attackers discover and exploit AI, and how Mindgard gives security teams visibility of that risk across models, agents and applications.
Overview
2:33
Three assumptions AI red teaming breaks
Three things security teams tend to believe before their AI has been red teamed. Their guardrails didn’t hold either.
Guardrails filter inputs they have seen before. Mindgard extracted Grok’s system prompt with soft elicitation, no known-bad strings required, after which the model offered dangerous guidance.
AI red teaming and penetration testing are not the same job. Traditional AppSec never touches AI-specific failure modes: prompt injection, jailbreak chains, system prompt leakage, agent and tool abuse.
AI systems fail silently. A leaked system prompt or a jailbroken agent throws no exception and trips no alert, until it is public. Mindgard has publicly identified 150+ AI vulnerabilities across leading AI systems, including Grok, ChatGPT and Google Antigravity.
None of this is carelessness. Most teams simply have no way to see their AI the way an attacker does.
What AI red teaming software actually tests
Mindgard chains domain-specific attacks across one-shot and multi-step interactions to reveal where guardrails hold, degrade and fail.
Prompt injection
Direct and indirect injection against chatbots, RAG pipelines and agent tool calls.
Jailbreak chains
Multi-step sequences that walk a model past its safety behaviour one turn at a time.
System prompt and data leakage
Soft elicitation and extraction techniques that surface hidden instructions and source data.
Tool and agent abuse
Attacks on how agents, tools, APIs and workflows interact, not just the model in isolation.
Agents, MCP and connected tools
Discovery and red teaming across models, agents, MCP/A2A servers and the tools they connect to.
Guardrail and filter probing
Where your guardrails hold, where they degrade under pressure, and where they fail outright.
One continuous red teaming loop, attacker to defender
Mindgard profiles your AI the way an adversary does, then gives your team what it needs to close the gaps.
Discover
Identify models, agents, connected tools and shadow AI across your stack.
Recon
Map the attack surface and profile each system’s behaviour.
Red team
Emulate real attacker behavior at scale to surface exploitable risk.
Defend
Get prioritised remediation guidance and runtime protection, in your workflow.
A demo of the platform, not a slide deck
We show you how Mindgard red teams AI systems like yours: prompt injection, jailbreaks, leakage and tool abuse, then walk through the exploitable findings it surfaces.
Security leaders and AppSec or 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. That is the whole risk.
Book a Demo →What you’ll see in the demo
DEMOUsed by security teams at global enterprises
Outcomes from published Mindgard customer stories.
AI security testing cycles reduced from weeks to hours across nine production AI models.
Separated real, exploitable AI risk from low-signal safety findings, with attacker evidence engineering teams could act on.
Uncovered AI-specific security risks, hardened its system prompt and strengthened security posture faster.
Every week your AI goes un-red-teamed is a week of unknown exposure.
The techniques Mindgard used to bypass ChatGPT’s image safeguards and to evade guardrails from Meta, Microsoft and NVIDIA are the same ones the platform runs against AI like yours. The only variable is who finds the gaps first.
Book a Demo →What security teams ask first
What is AI red teaming?
AI red teaming is the practice of attacking your own AI systems the way an adversary would: prompt injection, jailbreaks, system prompt and data leakage, and tool abuse, across models, agents, tools and workflows. Mindgard automates it and keeps testing as your AI evolves, rather than once a year.
How is AI red teaming different from penetration testing?
A pen test targets the application, infrastructure and code. AI red teaming targets the model and the system around it, where failures are probabilistic rather than deterministic. Mindgard secures complete AI systems, not just isolated models, capturing how agents, tools, APIs, data sources and workflows interact.
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, and showing where your guardrails hold, degrade and fail.
Is the red teaming automated or manual?
Automated and continuous. Mindgard emulates real adversary workflows, including reconnaissance, exploitation planning and execution, and keeps testing as models, configurations and capabilities change. Its attack techniques are strengthened by the team’s own vulnerability research and public disclosures.
Have you actually found anything in well-known AI systems?
Yes. 150+ AI vulnerabilities publicly identified and 30+ published disclosures, including a content safety bypass in OpenAI’s ChatGPT, guardrail evasion in Meta Prompt Guard, Microsoft Azure Prompt Shield and NVIDIA NemoGuard, and persistent code execution in Google’s Antigravity IDE. Each disclosure lists when it was reported to the vendor, and the techniques feed straight back into the platform. The full list is public.
Is the demo a sales pitch?
It is a product demo, not a slide deck. You see how Mindgard red teams AI systems and how the findings surface. If it is a fit, we talk next steps. If not, you still leave knowing more about how attackers approach AI like yours than you did before the call.
Is it enterprise-ready?
Yes. SOC 2 Type 2 compliant, built on a decade of Lancaster University research, and headquartered in Boston and London. Findings route into existing security tooling, ticketing systems and engineering workflows, and you can deploy through CI/CD, Burp Suite or a single click.
See Mindgard red team AI like yours
Book a demo. See how Mindgard red teams AI systems like yours, and what it finds.
Watch the full platform walkthrough
Twenty minutes inside the Mindgard AI red teaming platform: discovery, recon, attack runs and the findings they produce. If you want more detail before your demo, start here.
Full walkthrough
20:49