Blog: Findings From Our Key Research

Jailbroken Grok 4 autonomously tempts users to make explosives

Meet NEXUS, the unbound alter-ego an AI medical scribe wrote for itself

New capabilities enable continuous automated AI risk discovery, revealing the real AI attack surface and helping security teams conduct faster, more targeted security assessments.

Want to stay ahead in cybersecurity? This guide breaks down the best red teaming certifications and courses to help you think like an attacker, uncover vulnerabilities, and advance your career—whether you're a beginner or an expert.

A simple ruler experiment exposes a surprising blind spot in AI reasoning and why confident explanations don’t always reflect real-world understanding.

A growing field of AI security companies is helping organizations defend against adversarial threats, secure AI models, and automate cyber defense using advanced machine learning.

The rapid pace of AI adoption means many organizations are vulnerable to attacks like prompt injection and jailbreaks. Statistics show that AI red teaming helps to significantly reduce those risks.

AI governance templates are useful tools that document responsible AI principles and outline repeatable processes for managing AI risk and protecting data.

AI data security risks are increasing rapidly, and traditional security measures can't keep up. However, organizations that implement AI governance and AI-driven security automation can greatly reduce risks.

Offensive security tools are critical for identifying and fixing system vulnerabilities before real attackers exploit them.

This guide highlights ten leading tools—such as Mindgard, Burp Suite, and PentestGPT—that help organizations protect large language models and generative AI solutions from adversarial inputs and data manipulation.

The fastest and most cost-effective path to high-risk findings in AI Security

A medical chatbot can be hacked to give dangerous medical advice.

GenAI enhances cybersecurity by accelerating threat detection, automating responses, and generating actionable insights—transforming how security teams detect, understand, and defend against evolving threats. However, to be effective and safe, it must be used with human oversight to prevent risks like overreliance or model errors.

Discover the latest insights on AI security with Dr. Peter Garraghan, CEO of Mindgard, in this podcast episode. Learn about the security threats that neural networks face.

GenAI is transforming cybersecurity, enabling both attackers and defenders to scale their capabilities. While threat actors use AI for sophisticated phishing and automation, enterprises must adopt AI-driven security measures to stay ahead in the evolving cyber arms race.

This blog highlights concerns about AI safety benchmarks, which often reflect general capability improvements rather than true safety advancements, a practice termed "safetywashing."

Mindgard’s Policy capability filters noise and flags context-aware LLM vulnerabilities.

Mindgard is proud to announce its recognition as a winner of the Enterprise Security Tech 2024 Cybersecurity Top Innovations Award.

AI risk decisioning uses AI to continuously detect, evaluate, and respond to risks in real time, operationalizing AI governance frameworks and strengthening security and compliance across AI systems.

In a survey of over 500 cybersecurity professionals at RSA Conference and InfoSecurity Europe 2025, Mindgard uncovered a striking trend: security staff are using AI without approval. This rise in Shadow AI is creating a serious blind spot inside the very teams meant to protect the enterprise.

Dr. Peter Garraghan, CEO and co-founder of Mindgard, has been named Cybersecurity Innovator of the Year at the 2025 Cybersecurity Excellence Awards.

AI-powered attack simulations are redefining offensive security penetration testing by automating the discovery of vulnerabilities in both traditional systems and AI models—offering faster, more adaptive protection against today’s complex, evolving threats.

DeepSeek’s open-source model release highlights the power of collaboration in AI, sparking discussions on innovation costs, geopolitical implications, and the importance of thorough testing before integration.

Red teaming involves ethical hackers simulating real-world cyberattacks to test an organization’s ability to detect, respond to, and recover from advanced threats. Unlike traditional penetration testing, red team exercises go beyond set parameters to mimic malicious tactics, offering a comprehensive view of an organization’s security weaknesses.

Learn why effective AI red teaming must go beyond model attacks to focus on securing the entire application against real-world threats.

Mindgard’s attacker-aligned, research-driven approach provides security leaders with the insight and operational leverage needed to reduce risk, support compliance, and enable the confident use of AI.

We've been busy developing new features to add even more value to your Mindgard experience, and we're excited for you to try them out. Here's what's new: Improved Attack Page plus OWASP Top 10 and MITRE Atlas Techniques Mapping, Test your own Custom Datasets & Prompts, New Attacks Released, Introducing Mindgard Academy

Discover the latest insights on AI security with Dr. Peter Garraghan, CEO of Mindgard, in this podcast episode. Learn about threats, solutions, and how Mindgard can secure your AI systems.

Discover the latest insights on cybersecurity for AI in the TNW Podcast episode with Dr. Peter Garraghan. Learn about threats, solutions, and how Mindgard can help secure your AI systems.

AI risk management is the practice of identifying, mitigating, and continuously monitoring AI-specific threats, such as bias, model drift, adversarial attacks, and compliance failures, using frameworks like NIST AI RMF and ISO/IEC 42001 to ensure safe, ethical, and trustworthy AI systems throughout their lifecycle.

Securing an AI model requires a dedicated strategy that includes data management, input validation, access controls, watermarking, and specialized tools to defend against theft, manipulation, and evolving cyber threats.

Offensive security service providers help businesses outsmart cybercriminals by simulating real-world attacks, uncovering hidden vulnerabilities, and fortifying defenses before hackers strike.

OffSec certifications equip cybersecurity professionals to think like attackers and defend against real-world threats through rigorous, hands-on training.

Red team offensive security simulates real-world attacks to expose hidden vulnerabilities, with best results achieved by setting clear goals, using frameworks like MITRE ATT&CK, and partnering with expert vendors like Mindgard.

Welcoming Jim Nightingale to Mindgard as he expands our adversarial AI research, uncovering real-world vulnerabilities in modern AI systems and safety controls.

AI security templates standardize governance, risk management, and operational controls across the AI lifecycle, but they must be combined with testing, monitoring, and technical safeguards to protect against risks like prompt injection, data leakage, and model misuse.

AI red teaming templates provide structured prompts, protocols, and reporting frameworks that make adversarial testing more consistent, efficient, and effective at uncovering real-world vulnerabilities in AI systems before attackers exploit them.

AI data security templates provide structured, reusable frameworks that standardize how sensitive data is accessed, handled, and protected across AI systems, helping organizations reduce risks like prompt injection, data leakage, and unauthorized access when combined with technical controls and continuous validation.

Red team operations follow a structured five-phase approach—planning, reconnaissance, attack simulation, reporting, and remediation—ensuring organizations stay resilient against evolving threats.

Whether you're looking for tools to test AI models, safeguard sensitive data, or evaluate system defenses, this guide breaks down the top solutions and what to consider when choosing the right one.

As cyber threats evolve, organizations use penetration testing to stay ahead, and this guide spotlights 10 top providers—like BugCrowd, Deloitte, and Mindgard—offering expert services across industries and technologies.

Securing LLMs and GenAI applications requires specialized tools like Mindgard that offer capabilities such as red teaming, federated learning, and real-time monitoring to address AI-specific threats.

Protecting AI systems requires continuous implementation of best practices like red teaming, data validation, watermarking, access control, and audits to defend against evolving threats and ensure secure, trustworthy AI deployment.

AI risk management certifications and training programs are becoming essential as organizations adopt frameworks like NIST AI RMF, ISO/IEC 42001, and the EU AI Act, helping professionals gain the skills to identify, mitigate, and govern AI risks across compliance, ethics, and security.

Mindgard research shows how ChatGPT image safeguards were bypassed through memory manipulation, exposing safety concerns in AI generated content.

Red teaming is a proactive cybersecurity strategy where ethical hackers simulate real-world attacks—spanning technical, human, and physical vulnerabilities—to identify and address security weaknesses before malicious actors exploit them.

Mindgard research shows how extracting Google Search AI’s system instructions can undermine safety controls and enable session-level policy compromise.

Prompt injection is a system-level risk that lets attackers manipulate LLM behavior by exploiting how models interpret mixed instructions across prompts, content, and tools. Mitigation requires layered controls like least privilege, adversarial testing, and strict separation of untrusted content.

Prompt injection is the most persistent and dangerous threat facing LLMs, routinely bypassing guardrails and leading to risks such as data leakage, unsafe actions, and system compromise. Effective defense depends on continuous, adversarial testing across models, applications, and system integrations using automation, red teaming, and real-time monitoring rather than one-time reviews.

Prompt injection attacks exploit Copilot’s deep integration with enterprise systems to bypass guardrails through direct and indirect inputs. Layered defenses across prompts, data, tools, and workflows essential for reducing security and business risk.

DeepSeek’s reasoning-first architecture, exposed internal logic, and layered workflows expand the prompt injection attack surface, allowing attackers to manipulate reasoning, tools, memory, and guardrails rather than just user inputs. Mitigating these risks requires layered defenses, continuous monitoring, and adversarial testing that analyze reasoning, behavior, and system-level signals across the entire AI stack.

MCP servers turn prompt injection from a simple user-input risk into a distributed trust-boundary problem across tools, metadata, sessions, and external systems. Securing them requires layered controls, visibility across trust boundaries, and continuous adversarial testing to detect and contain indirect injection paths before they spread through agent workflows.

Prompt injection detection requires layered monitoring across inputs, context, behavior, outputs, tools, and memory because attacks rarely appear as a single signal. The most effective defenses combine technical controls, continuous adversarial testing, and human oversight to identify abnormal LLM behavior before it causes real-world harm.

Prompt injection and jailbreak attacks exploit different weaknesses in LLMs: prompt injection hijacks model behavior and downstream actions, while jailbreaks bypass safety guardrails to produce restricted outputs. Because these attacks often propagate through tools, pipelines, and workflows, effective LLM security requires layered defenses across model, application, and system layers.

Prompt injection attacks exploit missing trust boundaries in LLMs to override instructions and cause real-world harm, and cannot be stopped with prompts alone. Effective defense requires layered controls for detection, mitigation, and protection enforced outside the model to limit damage and prevent escalation.

A red teaming checklist provides a structured approach to cybersecurity testing, ensuring all critical aspects—such as scope, reconnaissance, execution, and mitigation—are thoroughly covered.

Mindgard AI Security Testing Part of $1.7 Billion in VC Investments Into AI TRiSM Startups

A technical analysis of Lumo AI’s end-to-end encryption model, API behavior, and Mindgard’s release of open-source pyLumo tooling.

Sharpen your AI model safety with single-shot and multi-turn techniques.

Prompt injection exploits LLMs’ lack of trust boundaries and grows more dangerous as models gain context, tools, and autonomy. The most harmful techniques manipulate instruction handling to trigger data leaks and unauthorized actions across workflows.

Prompt injection attacks exploit LLMs’ lack of trust boundaries, allowing malicious instructions to override safeguards in ChatGPT, especially in RAG and agentic systems. Reducing risk requires layered defenses such as policy enforcement, prompt hardening, continuous testing, and output filtering.

Indirect prompt injections embed malicious instructions in trusted content, letting attackers hijack LLMs without direct user input. Effective prevention requires layered defenses, including input sanitization, strict instruction boundaries, least-privilege tools, and continuous testing.

Prompt injection attacks exploit LLMs’ inability to separate trusted instructions from untrusted content. The only effective defense is layered security that limits impact when injections slip through.

Prompt injection is a systemic risk where LLMs follow malicious instructions hidden in inputs because they lack native trust boundaries. As models gain tools, memory, and autonomy, these attacks can trigger real data leaks and unauthorized actions unless controls exist outside the model.

The Mindgard platform identified high severity vulnerabilities in TheLibrarian.io platform

The Mindgard solution identified two vulnerabilities in the Zed IDE and our team worked with the developers on a coordinated remediation process.

We show how we revealed Sora 2's system prompt by experimenting across multiple modalities, including text-to-image, ASCII and glyph renderings, video, audio captions, and transcripts.

In this post, we’ll show how a clever attacker can slip prompt injections into source files, turning a helpful partner into a hazard.

Read how we extracted Sora’s system prompt using cross-modal prompting techniques.

Mindgard discovered that the Manus AI browser extension is for all intents and purposes, a full browser remote control backdoor.

Discover a security issue within Azure AI Content Safety guardrails that Mindgard has discovered and reported to Microsoft.

Agentic AI creates new, evolving attack surfaces that require strict access controls and continuous monitoring to prevent large-scale security incidents.

Decisions made by high-risk AI systems can significantly affect people's safety, rights, or access to essential services, making early identification and strong governance critical.

High-risk AI requires continuous, structured oversight and safeguards to prevent bias and system failures that could lead to legal, reputational, and financial harm.

AI model risk management helps organizations proactively identify, monitor, and mitigate ethical, operational, security, and compliance risks across the entire AI lifecycle using governance frameworks, continuous testing, and automated tools to keep models safe, reliable, and accountable.

The NIST AI Risk Management Framework (AI RMF) provides voluntary but widely adopted guidance to help organizations identify, map, measure, and manage AI risks across the lifecycle, enabling more trustworthy, accountable, and compliant AI systems aligned with emerging regulations like the EU AI Act and ISO/IEC standards.

Agentic AI can act autonomously at superhuman speed, so organizations must apply governance frameworks, access controls, human oversight, audit trails, and continuous red teaming to prevent cascading failures, compliance breaches, or security incidents as these systems scale.

Proactive AI risk mitigation—through regular assessments, stress testing, data validation, access controls, and continuous monitoring—helps organizations prevent vulnerabilities while maintaining compliance, transparency, and resilience under frameworks like ISO/IEC 42001 and NIST AI RMF.

AI risk assessments provide a structured, continuous process for identifying, ranking, and mitigating security, bias, and compliance threats across the AI lifecycle, ensuring systems remain safe, trustworthy, and compliant as they evolve.

ISO/IEC 23894 is a voluntary international standard that provides a practical, lifecycle-based framework for identifying, assessing, and mitigating AI-specific risks, complementing governance standards like ISO/IEC 42001 and the NIST AI RMF to help organizations operationalize responsible, secure, and compliant AI practices.

ISO/IEC 42001 is one of the first auditable standards for AI management systems, providing organizations with a structured, certifiable framework to govern AI ethically, manage risks like bias and security vulnerabilities, and ensure continuous oversight and compliance across the AI lifecycle.

AI Security Posture Management (AI-SPM) is a continuous, lifecycle-wide approach to identifying, monitoring, and mitigating AI-specific threats—like prompt injection, model drift, and data poisoning—through asset discovery, risk scoring, policy enforcement, and automated remediation.

Mindgard, the leading provider of Artificial Intelligence security solutions, today announced the appointment of James Brear as Chief Executive Officer.

Red teaming, a concept originally rooted in military strategy, involves deploying a dedicated team to challenge an organization’s defenses. When applied to AI, this approach goes beyond traditional testing methodologies by simulating real-world adversarial scenarios to evaluate how AI systems perform under pressure.

This guide highlights six top courses that cover hands-on techniques, ethical frameworks, and policy development to address the evolving risks of AI integration.

AI is reshaping business operations but brings unique security threats that require specialized defenses, governance, and continuous testing across data, models, and integrations.

AI security defends machine learning and generative systems from evolving threats like data poisoning and model theft through full-lifecycle protections and proactive testing.

Maximize your cybersecurity with BAS vs red teaming—learn how automation and real-world attack simulations complement each other for stronger defenses.

Measuring the effectiveness of a red teaming assessment is crucial for ensuring continuous improvement in cybersecurity defenses.

Attackers use generative AI for stealthier breaches, while organizations fight back with AI-driven detection, diverse data, human oversight, and retraining to stay resilient.

Multi-agent AI systems boost efficiency but create unique risks, and organizations can secure them by proactively applying frameworks, automated tools, simulated attacks, guardrails, and continuous monitoring through AI threat modeling.

AI agents face novel threats like memory poisoning, model extraction, identity spoofing, and multi-agent collusion, making proactive AI threat intelligence essential to detect anomalies, simulate adversarial attacks, and defend against emerging risks.

Automated AI threat hunting uses machine learning and real-time monitoring to provide 24/7 protection, faster response, and more accurate detection of evolving threats than traditional defenses.

Discover the critical importance of defending AI models against adversarial attacks in the cybersecurity landscape. Learn about six key attack categories and their consequences in this insightful article.

AI systems are vulnerable across models, data, infrastructure, and governance. Resources like the AI Vulnerability Database (AVID) and Mindgard help organizations identify, prioritize, and defend against these risks.

AI vulnerability management in DevOps requires a proactive approach that combines continuous detection, data validation, frameworks, versioning, developer training, and adversarial testing to reduce risks, protect AI systems, and maintain business continuity, compliance, and trust.

AI application vulnerability scanning identifies risks traditional tools miss. Tracking six practical metrics helps security teams strengthen defenses, ensure compliance, and prevent costly breaches.

AI vulnerability assessments identify and remediate risks across the AI lifecycle, using a five-step process to strengthen resilience and protect sensitive data.

An overview of the third edition of the Gartner AI TRiSM (Trust, Risk and Security Management) Market Guide.