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.

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

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.

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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 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 2025 Gartner Hype Cycle for Application Security and Mindgard’s inclusion.

The OWASP AI Security and Privacy Guide outlines essential practices, from treating outputs as untrusted to securing plugins and limiting autonomy, to help teams proactively defend their AI models across the full lifecycle.

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 security use cases like continuous red teaming, threat detection, automated response, predictive analysis, and model explainability help organizations proactively identify and mitigate risks across the AI lifecycle. As traditional tools struggle with threats such as model manipulation and poisoned data, AI-specific defenses and guardrails are now essential for protecting modern systems.

The rapid rise of generative AI (GenAI) has introduced serious data security risks, with most organizations experiencing breaches tied to sensitive information exposure. Securing GenAI systems requires AI-specific protections like prompt validation, output filtering, lifecycle encryption, and continuous offensive testing.

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.

We are excited to welcome two strategic leadership additions to strengthen our R&D and GTM capabilities. Renowned security researcher Aaron Portnoy joins as Head of Research & Innovation, while SaaS veteran Tom Axbey joins the Board of Directors.

An AI security assessment helps organizations identify and mitigate unique risks through a structured process of inventorying assets, evaluating vendors, conducting risk analysis, and implementing tailored controls and response plans.

AI-generated code can boost developer productivity but also introduces major risks (like insecure code, legal exposure, IP leakage, and skill atrophy) that require strong review processes, governance policies, and dedicated AI security tools to mitigate.

AI agents introduce unique security threats—like memory poisoning, prompt injection, and privilege misuse—that require targeted defenses such as RBAC, input validation, rate limiting, and continuous monitoring to ensure safe deployment and operation.

AI application security requires specialized strategies—like strong data governance, secure architecture, hardened infrastructure, continuous threat monitoring, and AI red teaming—to defend against threats such as data poisoning, model theft, and adversarial manipulation that traditional security tools can’t fully address.

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 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.

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.

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.

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.

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.

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

AI guardrails are layered safeguards that ensure generative AI systems behave ethically, safely, and within organizational or regulatory boundaries by filtering training data, aligning model behavior, and enforcing post-deployment controls.

Guardrails help secure large language models by preventing harmful outputs and misuse through clear policies, limited access, and expert-led testing and monitoring.

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

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

Generative AI is reshaping cybersecurity by enhancing threat detection, simulating attacks, and automating responses—making defenses faster and more adaptive.

AI data security uses machine learning, automation, and real-time monitoring to proactively detect and prevent cyber threats, protect sensitive information, and secure AI models from manipulation.

We’re excited to introduce new capabilities to the Mindgard solution: Custom Dataset Generation, Prompt Repetitions, Attack Playground and Decode & Answer. 

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.

AI is advancing offensive security through faster, adaptive simulations, but it also brings risks like model manipulation, poor data quality, and ethical concerns.

Offensive security flips the script on traditional cybersecurity by simulating real-world attacks to expose vulnerabilities before real hackers can strike.

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

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.

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

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

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.

AI penetration testing uncovers vulnerabilities like data poisoning, prompt injection, and model inversion, helping organizations secure their systems beyond what traditional security measures can offer.

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.

Targeted penetration testing is essential for securing AI chatbots against threats like data breaches and prompt injection, using strategies like input validation and tools such as Mindgard to identify and fix vulnerabilities.

Continuous AI pentesting is an automated, real-time security testing approach that continuously monitors AI models for vulnerabilities like adversarial attacks, data poisoning, and bias.

Mindgard, a pioneer in AI security testing, has been recognized as the winner of the Best Cybersecurity Startup and Best AI Security Solution at the 2025 Cybersecurity Excellence Awards.

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

Case studies highlight how businesses in various industries have leveraged red teaming to improve security readiness, enhance monitoring systems, and refine defensive strategies against evolving threats.

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

With emerging threats like biometric hacks, drone surveillance, and smart building exploits, integrating physical and cyber security has become essential for organizations to maintain a robust defense strategy.

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 is thrilled to announce that we have been nominated for the NVIDIA GTC 2025 Poster Award! As one of the premier global conferences for AI, accelerated computing, and security innovation, NVIDIA GTC showcases groundbreaking research and technological advancements from industry leaders, academia, and startups.

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

Red teaming simulates real-world cyberattacks to identify vulnerabilities, using techniques like social engineering, physical penetration, and AI-specific methods such as adversarial attacks and data poisoning.

Red teaming companies simulate real-world attacks to test cybersecurity defenses, compliance, and crisis response, offering unbiased assessments and tailored security recommendations.

The OpenAI Red Teaming Network is a collaborative initiative that enlists external experts from various fields to rigorously test OpenAI’s AI models for vulnerabilities, bias, and ethical concerns.

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

Red teams in cybersecurity simulate real-world attacks to identify vulnerabilities, while purple teams bridge offensive and defensive efforts to enhance security collaboration.

Google’s red team is a specialized security unit that proactively simulates cyberattacks to uncover vulnerabilities in Google’s infrastructure, helping to fortify defenses before malicious actors can exploit weaknesses.

This article introduces key experts in the AI security and AI red teaming field—researchers, engineers, and policy advocates—who are shaping the future of AI security. By following their work, professionals and enthusiasts can stay ahead of emerging threats and best practices for building secure, ethical AI systems.

Microsoft’s AI Red Team is a specialized group dedicated to stress-testing AI models for security vulnerabilities, biases, and adversarial threats. As AI becomes central to industries like healthcare and finance, this team plays a crucial role in ensuring fairness, reliability, and compliance with Responsible AI Principles—making AI safer and more trustworthy for everyone.

Red teaming exercises simulate real-world cyberattacks to expose vulnerabilities in an organization’s security defenses, from employee awareness to AI platform resilience. This guide breaks down the key processes, real-world examples—like phishing simulations and insider threat tests—and AI security challenges, showing how red teaming helps organizations stay ahead of evolving threats.

Want to see how cybersecurity pros stay ahead of hackers? This guide breaks down the roles of red, blue, and purple teams—how they simulate, defend against, and improve responses to cyberattacks.

Think your cyber defenses are strong? A red team attack simulation puts them to the ultimate test—mimicking real-world adversarial tactics to uncover vulnerabilities before real hackers do. This article dives into how these simulations work, their benefits, and why they’re essential for securing everything from enterprise systems to AI platforms.

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.

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.

Discover how the MIT AI Risk Repository is helping AI red teamers and pen testers tackle complex security challenges with actionable insights.

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.

LLM Red Teaming involves simulating adversarial attacks to uncover vulnerabilities such as bias, security risks, and information leakage, ensuring models are secure, ethical, and robust before deployment. Techniques like automated testing, prompt injection, bias testing, and data poisoning simulations help organizations proactively address threats and improve AI safety at scale.

Unlike traditional red teaming, which occurs periodically, CART operates 24/7, reducing human error, enabling scalability, and allowing immediate threat mitigation, making it a critical tool for securing modern digital and AI-driven environments.

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

This post compares and contrasts the recent UK and US governments' ambitious AI development plans.