Resources: Navigating the AI Security Landscape

A range of resources including research papers, webinars, and company news focused on AI Security.

Inside OpenAI Sora 2: Uncovering System Prompts Driving Multi-Modal LLMs

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.

From Prompt to Pwn: Cline Bot AI Coding Agent Vulnerabilities

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

Continuous AI Red Teaming: Why S&P Global Says It’s Critical to Securing the AI Era

S&P Global Coverage Initiation: Mindgard’s continuous AI red teaming looks to secure models and applications

Introducing Mindgard MITRE ATLAS™ Adviser

MITRE ATLAS™ Adviser in Mindgard that helps standardise AI red teaming reporting.

Webinar: Prompt. Inject. Shell. Repeat - Exploiting Blind Spots within AI Guardrails

AI guardrails are often used as the first line of defense within AI systems, however how effective are they in practice against actual attackers?

Hunting AI Application Vulnerabilities With Burp

In this article we’ll walk through hunting for AI application vulnerabilities. We’ll use Mindgard to find application vulnerabilities in a deliberately-vulnerable LLM lab application made available by PortSwigger.

Analyst Report: OWASP LLM and Generative AI Security Solutions Landscape

The LLM and Generative AI Security Solutions Landscape is an industry report developed by OWASP that maps out key vendors and solutions in the AI security space. This landscape provides a comprehensive view of tools and technologies that help organizations safeguard their AI-powered applications.

Research: Shadow AI is a Blind Spot in Enterprise Security, Including Among Security Teams

At RSA Conference 2025 and InfoSecurity Europe 2025 we surveyed over 500 cybersecurity professionals to assess emerging threats in enterprise environments. The findings reveal a growing and often overlooked risk: security professionals using generative AI tools without approval, a trend known as Shadow AI.

Talk: Deconstructing AI Risk — From Research to Real-World Exploits

In this talk, Peter Garraghan demonstrates how adversaries are already exploiting AI systems and why current security practices are often ill-equipped to stop them.

Webinar: Test AI Systems, Not Models

In this webinar, Dr. Peter Garraghan takes the audience on a deep dive into the underbelly of AI vulnerabilities, exposing the gaps within traditional AI security approaches and demonstrating why application-level AI security must be a priority.

Analyst Report: Gartner AI TRiSM Market Guide

Gartner's AI Trust, Risk, and Security Management (AI TRiSM) framework provides a structured approach to managing AI risks while maintaining transparency and accountability.

AI Red Teaming for Image Models

With this update, Mindgard’s platform and CLI have been updated to support image models.

PINCH: An Adversarial Extraction Attack Framework for Deep Learning Models

PINCH is an efficient and automated extraction attack framework.

Mindgard Recognized as UK's Most Innovative Cyber SME 2024 at Infosecurity Europe

Report: Cyber Security for AI Recommendations

Model Leeching: An Extraction Attack Targeting LLMs

Compilation as a Defense

Enhancing DL Model Attack Robustness via Tensor Optimization