Expert-led, platform-backed

AI Red Teaming Services

Mindgard's researchers attack your AI systems the way a motivated adversary would: prompt injection, agent hijacking, tool abuse, data extraction and guardrail bypass, chained into the attack paths that matter to your business. You get reproducible findings, a remediation roadmap and a platform that keeps testing after we leave.

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See how the platform automates red teaming

A Mindgard AI security lead replies within one business day.

150 published AI vulnerability disclosures

Nine production models secured for a US insurance brokerage

A decade of AI security research at Lancaster University

SOC 2 Type 2

Sample readout: attack path 3 of 7

CriticalIndirect prompt injection via retrieved PDF
Support agent read attacker-controlled document, then called update_customer_email on 1,204 records.
OWASP LLM01 · LLM06 · ATLAS AML.T0051

HighSystem prompt and RAG source leakage
Full system prompt plus 14 internal document titles returned in 3 turns.
OWASP LLM07 · ATLAS AML.T0057

MediumGuardrail bypass, multilingual
Content filter missed 62% of Portuguese-language jailbreak variants.
OWASP LLM01 · ATLAS AML.T0054

Illustrative findings, not customer data.

Global insurance brokerage

Healthcare AI platform

Global financial institution

Semiconductor manufacturer

Enterprise technology services

What an AI red teaming engagement delivers

Every Mindgard red team engagement ends with five things your security team can act on the same week:

Attack narrativesEach successful attack path written up end to end: entry point, technique, what the attacker reached and the business impact.

Reproducible transcriptsThe exact prompts, tool calls and payloads that worked, so your engineers can replay them and confirm the fix.

Severity-ranked findingsEvery issue rated by exploitability and impact, mapped to OWASP Top 10 for LLM Applications and MITRE ATLAS technique IDs.

Control-effectiveness verdictsFor each guardrail, filter and monitoring control in scope: did it detect, did it block or did it miss.

Remediation roadmap and retestFixes ordered by risk reduction, then a retest window to confirm they hold.

Regression tests in the platformThe attacks that worked become tests that run on every model or prompt change. See the platform.

What we red team

Mindgard red teams the whole AI system, not just the model. Scope typically covers:

LLM applications. Chat assistants, copilots and RAG pipelines, including the retrieval layer, system prompts and output handling.
AI agents and tool use. Agents that call APIs, browse, write code or act on tickets. We test tool poisoning, indirect prompt injection through retrieved content, privilege escalation across tools and MCP server abuse, following the OWASP Agentic AI threats and mitigations taxonomy.
Guardrails and safety layers. Input and output filters, content classifiers and policy engines, tested for bypass and for the false-positive rate that pushes teams to switch them off.
Models. Foundation, fine-tuned and open-weight models: jailbreaks, extraction, membership inference, model inversion and training-data leakage.
Multimodal systems. Image, audio and document inputs used as injection carriers.
Runtime detection. Whether your monitoring sees the attack while it happens, feeding Mindgard AI Runtime Protection if you use it.

Not sure whether you need a red team or a scoped pentest? Compare the two engagements or read the difference in plain terms.

Attack techniques we use, mapped to OWASP and MITRE ATLAS

The techniques below are the core of every engagement. Each maps to a public taxonomy so your findings speak the same language as your auditors and your engineering backlog.

Technique	What we do	OWASP LLM Top 10 (2025)	MITRE ATLAS
Direct and indirect prompt injection	Override system prompts through user input, retrieved documents, web pages, emails and tool outputs	LLM01	AML.T0051
Jailbreaking and guardrail bypass	Role-play, encoding, multi-turn and multilingual attacks against safety filters	LLM01LLM05	AML.T0054
Sensitive data extraction	Pull system prompts, RAG documents, PII and credentials out of responses	LLM02LLM07	AML.T0057
Agent and tool abuse	Hijack agent goals, chain tools for privilege escalation, poison MCP tool descriptions	LLM06	AML.T0053
Model extraction and inference	Reconstruct model behavior, infer training-set membership, invert outputs to inputs	LLM10	AML.T0024
Data and supply chain poisoning	Tamper with RAG corpora, fine-tuning data and third-party model artifacts	LLM03LLM04	AML.T0020
Denial of wallet and service	Force runaway token use, recursive tool calls and cost amplification	LLM10	AML.T0029

Sources: OWASP Top 10 for LLM Applications 2025, MITRE ATLAS.

How an engagement runs

Five phases. Typical elapsed time from kickoff to final readout is four to six weeks for a single AI application.

PHASE 1 · WEEK 1Scoping and rules of engagement

Systems in scope, attacker profiles to emulate, success criteria and safety controls for production or a mirror. No model weights required for black-box scope.

PHASE 2Threat modeling

We map your AI attack surface: entry points, data flows, tool permissions, trust boundaries. Output is a threat model your team keeps.

PHASE 3 · 2 TO 3 WEEKSAdversarial testing

Researchers run manual, objective-driven attacks while the platform runs the automated library in parallel. Humans chain what automation finds into full attack paths.

PHASE 4Findings and readout

Severity-ranked report, attack narratives, reproducible transcripts and a live readout with your security and engineering leads.

PHASE 5Remediation support and retest

We validate fixes and load the successful attacks into the platform as regression tests so they cannot silently return.

A named Mindgard lead runs the engagement from scoping to retest. Scope yours.

Why security teams choose Mindgard

Researchers, not a rebadged pentest team

Mindgard grew out of more than ten years of AI security research at Lancaster University. The people running your engagement publish the vulnerability disclosures that vendors patch.

Human plus platform

Manual red teaming finds the novel attack. The Mindgard platform runs thousands of attack variants and keeps running them after the engagement. You get both, and the second one does not expire.

Proof from regulated customers

A US insurance brokerage used Mindgard to secure nine production AI models and cut testing cycles from weeks to hours. Read more customer stories.

Findings your auditors accept

Every issue carries an OWASP and MITRE ATLAS reference and maps to the NIST AI RMF Measure function, so the report doubles as governance evidence.

Red teaming for compliance and AI governance

Red teaming is now a named obligation, not a nice-to-have. Article 55 of the EU AI Act requires providers of general-purpose AI models with systemic risk to perform and document adversarial testing. ISO/IEC 42001 asks for AI risk assessment and treatment with evidence. The NIST AI RMF Measure function expects AI systems to be evaluated for security and resilience against adversarial input.

A Mindgard red team engagement produces the evidence each of these asks for: the threat model, the test record, the findings mapped to a public taxonomy and the retest confirming remediation. If your governance team needs a specific artifact, tell us during scoping and we build the report to fit. Talk to us about compliance-ready red teaming.

Who this is for

Enterprises deploying LLM apps and agentsYou have copilots or agents touching customer data and need an attacker's view before scale-up.

Teams shipping AI productsYour AI feature is the product. A public jailbreak or data leak is a headline, not a ticket.

Security leaders answering the board or a regulatorYou need documented adversarial testing with named frameworks behind it.

Teams already running automated testingThe platform catches the known. A human red team finds the attack nobody wrote a test for yet.

AI red teaming services FAQ

Why should enterprises trust Mindgard to secure their AI systems?

Mindgard was created from more than a decade of AI security research at Lancaster University and embeds offensive security and AI research expertise directly into its platform. Its technology has identified more than 150 publicly disclosed vulnerabilities across prominent AI systems. This research-led, attacker-aligned approach gives enterprises evidence-based insight into how their AI could be exploited and what they need to do about it.

Does Mindgard only identify AI vulnerabilities, or does it also help teams remediate and defend against them?

Mindgard goes beyond identifying vulnerabilities. It validates which weaknesses are exploitable, explains their potential impact, and provides evidence and guidance to help teams close them. Teams can retest fixes, verify that defenses remain effective, and deploy runtime protections where needed. This creates a continuous process of finding, proving, remediating, and verifying AI risk as systems change.

How quickly can Mindgard be deployed and integrated into existing security workflows?

Mindgard can be operational in minutes through APIs, CI/CD pipelines, Burp Suite, or a single-click workflow. It integrates AI security into existing development, engineering, and security processes so teams can assess systems before deployment and continuously in production. Mindgard reduces AI risk assessment from weeks to hours without requiring organizations to build a specialist AI security function or assemble and maintain multiple testing tools.

How does Mindgard compare with open-source AI security tools such as Garak, PyRIT, and Promptfoo?

Garak, PyRIT, and Promptfoo give technical teams useful frameworks for building and running AI security tests. However, organizations must configure, operate, maintain, and interpret these tools themselves. Mindgard provides an enterprise-ready offensive security platform that can be operational in minutes, reduces AI risk assessments from weeks to hours, and adds continuous testing, risk prioritization, remediation guidance, workflow integrations, executive reporting, and GRC evidence.

How does Mindgard differ from other AI security platforms such as Noma, Gray Swan, Straiker, and HiddenLayer?

These companies offer overlapping capabilities across AI discovery, adversarial testing, safety evaluation, guardrails, runtime protection, and posture management. Mindgard differentiates through offensive security built specifically for AI systems and agents. Its attacker-style reconnaissance, adaptive attack chaining, proprietary vulnerability intelligence, and focus on proven exploitability help teams identify the vulnerabilities most likely to cause breaches and take evidence-based action to close them.

How is Mindgard different from AI guardrails and AI firewalls?

AI guardrails and firewalls apply policies to prompts, responses, or actions at runtime. Mindgard tests whether those controls actually work. It conducts adaptive attacks to identify where guardrails hold, degrade, or fail as AI systems change. Mindgard also goes beyond the prompt layer by assessing agents, tools, permissions, APIs, data, and infrastructure, providing evidence teams can use to strengthen and verify their defenses.

What makes Mindgard’s offensive security approach different?

Mindgard begins by examining AI systems the way an attacker would. Its agent-native reconnaissance maps models, agents, tools, instructions, and behaviors before attacks are planned and executed. Intelligence from more than 150 publicly disclosed AI vulnerabilities continuously strengthens its proprietary knowledge base. This helps Mindgard prioritize proven attack paths and exploitable defensive gaps instead of producing large volumes of generic test results.

How does the Mindgard AI security platform work?

Mindgard follows a continuous discover, recon, attack, and defend process. It identifies AI assets and attack surfaces, performs reconnaissance to understand system behavior, and uses adaptive attacks to find and validate exploitable vulnerabilities. Mindgard then provides evidence and remediation guidance, verifies whether defenses work, and continuously reassesses systems as models, prompts, tools, configurations, and policies change.

What types of AI applications, agents, and models does Mindgard secure?

Mindgard secures AI applications, agents, models, chatbots, agentic workflows, APIs, connected tools, and supporting infrastructure. It can assess systems built with open-source or managed models, including RAG applications, tool-using agents, and systems connected through MCP or A2A servers. Mindgard examines the complete AI system because attackers exploit interactions between models, agents, tools, data, and infrastructure, not just the model in isolation.

Who is Mindgard built for?

Mindgard is built for security and engineering teams accountable for AI systems in development and production. This includes application security, product security, red team, AI and ML engineering, governance, risk, and compliance teams. It is particularly valuable for organizations that need greater visibility into AI risk but lack the specialist knowledge or resources required to assess rapidly changing AI systems manually.

Is Mindgard an AI red teaming tool or an AI security platform?

Mindgard is an AI security platform with advanced AI red teaming at its core. It goes beyond running predefined attacks by discovering AI assets, performing attacker-style reconnaissance, planning adaptive attack paths, validating defensive gaps, and helping teams remediate them. Mindgard also supports runtime protection, security reporting, and continuous policy compliance across the AI lifecycle.

What is Mindgard and what does it do?

Mindgard is an offensive security platform purpose-built for AI systems and agents. It helps security and engineering teams discover the vulnerabilities most likely to cause breaches, validate where defenses fail, and understand how to close those gaps. Mindgard provides continuous visibility into AI risk across development and production as applications, agents, models, tools, and policies change.

Still have questions?

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See what an attacker sees

Book a demo to watch the Mindgard platform attack a live AI system, or scope a red team engagement with our researchers. Either way you leave with a clearer picture of your AI risk than you had this morning.

Book a Demo

Scope a red team

A Mindgard AI security lead replies within one business day to confirm scope and set a 30-minute call.