Mindgard is an AI Red Teaming vendor based in London, UK. Its flagship product is Mindgard AI Security Platform (Discover, Recon, Attack, Defend). Against the OWASP LLM Top 10 (2025) we score it Covered on 4 of 10 risks, Partial on 4, with 6 MITRE ATLAS techniques mapped as of . It's independent.
- 4/10
- Risks covered
- 4
- Partial
- 6
- ATLAS techniques
- $11.6M+
- Funding
- Active
- Status
At a glance
- Category
- AI Red Teaming
- Headquarters
- London, UK
- Founded
- 2022
- Employees
- 11-50
- Flagship product
- Mindgard AI Security Platform (Discover, Recon, Attack, Defend)
- Deployment
- SaaS cloud platform with API and CI/CD integration; Burp Suite integration
- Best for
- Security teams wanting continuous automated red teaming and attack-surface mapping for LLMs, image models and agents
- Funding to date
- $11.6M+
- Last round
- Series A ($8M) · 2024-12
- Status
- Active
Facts as of .
What does Mindgard do?
Continuously attacks your LLMs and agents the way an adversary would, then hands you the fixes; automated red teaming that began as Lancaster University research. It ships as SaaS cloud platform with API and CI/CD integration; Burp Suite integration, and it's built for security teams wanting continuous automated red teaming and attack-surface mapping for LLMs, image models and agents. It's a weaker fit for buyers needing inline runtime blocking as the primary control rather than offensive testing.
Which OWASP LLM Top 10 risks does Mindgard cover?
Mindgard's strongest verdict is LLM01Prompt Injection. The two tables below come from our editorial read of Mindgard's public documentation as of (it's what the docs support, not a hands-on lab test), and this row also sits in the full matrix across all 21 vendors.
| OWASP LLM Risk | What it is | Mindgard coverage | How it's addressed | Source |
|---|---|---|---|---|
| LLM01 Prompt Injection | User or hidden input overrides the model's rules or intended behavior. | Covered | Tests prompt injection and jailbreaking (guardrail busting) as core red-team attack classes. | https://mindgard.ai/ |
| LLM02 Sensitive Information Disclosure | The model leaks secrets, personal data, or proprietary content in its output. | Partial | Recon/Attack probe for sensitive-data extraction; testing-oriented rather than runtime prevention. | https://mindgard.ai/ |
| LLM03 Supply Chain | Compromised models, datasets, plugins, or dependencies add risk before runtime. | Partial | Discover produces AI-BOM and surfaces shadow AI; not full dependency supply-chain scanning. | https://mindgard.ai/ |
| LLM04 Data and Model Poisoning | Tampered training or fine-tuning data corrupts how the model behaves. | Covered | Offensive Security red-teaming explicitly tests for training-data/model poisoning as a core simulated attack class, with dedicated research/blog content on detection. | https://mindgard.ai/blog/training-data-poisoning |
| LLM05 Improper Output Handling | Downstream systems trust model output without checking it, enabling injection or code execution. | Unverified | Output-handling weaknesses not explicitly documented. | None found |
| LLM06 Excessive Agency | An agent holds more permissions, tools, or autonomy than the task needs. | Partial | Agent security testing exercises tools/workflow exploitation and inter-agent behaviors. | https://mindgard.ai/ |
| LLM07 System Prompt Leakage | The system prompt or the secrets inside it get exposed to users. | Covered | Explicitly tests system-prompt/hidden-instruction extraction (e.g. published OpenAI Sora disclosure). | https://mindgard.ai/ |
| LLM08 Vector and Embedding Weaknesses | Flaws in RAG stores let attackers poison, extract, or infer data. | Unverified | No public statement on vector/embedding-store testing. | None found |
| LLM09 Misinformation | The model produces false or fabricated content that users act on. | Covered | DAST-AI platform is documented as surfacing real-world misuse patterns including hallucinations as part of adversarial red-team testing. | https://mindgard.ai/blog/what-is-ai-red-teaming |
| LLM10 Unbounded Consumption | Uncontrolled requests drive cost, denial of service, or model extraction. | Partial | Mindgard published its own research demonstrating red-team testing of resource-exhaustion/unbounded-consumption attacks (reasoning-loop exhaustion) against DeepSeek-R1, showing the capability exists but not documented as a broad, general unbounded-consumption test category. | https://mindgard.ai/blog/deepseek-r1s-susceptibility-to-exhaustion-attacks |
| ATLAS Technique (ID) | Tactic | Mindgard coverage | Notes | Source |
|---|---|---|---|---|
| LLM Prompt Injection (AML.T0051) | Initial Access / Execution | Covered | Core attack class in automated red-team campaigns. | https://mindgard.ai/ |
| LLM Jailbreak (AML.T0054) | Privilege Escalation / Defense Evasion | Covered | Guardrail-busting/jailbreak testing across models. | https://mindgard.ai/ |
| Craft Adversarial Data (AML.T0043) | ML Attack Staging | Covered | Research-driven adversarial input generation drives the Attack module. | https://mindgard.ai/ |
| LLM Data Leakage (AML.T0057) | Exfiltration | Partial | Tests for sensitive-data and system-prompt extraction. | https://mindgard.ai/ |
| ML Model Inference API Access (AML.T0040) | ML Model Access | Covered | Attacker-style reconnaissance profiles models/agents via inference access. | https://mindgard.ai/ |
| External Harms (AML.T0048) | Impact | Partial | Compliance reporting frames business/brand impact of successful attacks. | https://mindgard.ai/ |
Is Mindgard independent, and how is it funded?
Mindgard is an independent company as of . It has raised $11.6M+ to date, most recently a Series A ($8M) dated 2024-12. Lead investors: .406 Ventures, IQ Capital.
Mindgard alternatives
The closest alternatives we track in AI Red Teaming are CalypsoAI, Giskard, Robust Intelligence. On the open-source side, garak covers similar ground.
- Mindgard vs Lakera
- Mindgard vs CalypsoAI
- Mindgard vs Robust Intelligence
- Mindgard vs Giskard
- Mindgard vs TrojAI
Frequently asked questions
What is Mindgard used for?
Mindgard is an AI Red Teaming vendor. Its flagship product is Mindgard AI Security Platform (Discover, Recon, Attack, Defend). It ships as SaaS cloud platform with API and CI/CD integration; Burp Suite integration, and it's built for security teams wanting continuous automated red teaming and attack-surface mapping for LLMs, image models and agents.
Is Mindgard independent or acquired?
Mindgard is an independent company as of 2026-07-13 and has not been acquired.
How many OWASP LLM Top 10 risks does Mindgard cover?
We score Mindgard as Covered on 4 of the ten OWASP LLM Top 10 (2025) risks, with 4 more Partial, as of 2026-07-13. The table on this page breaks down every risk, including the ones marked Not covered or Unverified.
How is Mindgard deployed?
Mindgard ships as SaaS cloud platform with API and CI/CD integration; Burp Suite integration.
What are the best alternatives to Mindgard?
The closest AI Red Teaming alternatives llmthreat tracks are CalypsoAI, Giskard, Robust Intelligence. On the open-source side, garak covers similar ground.