Why You Need Red Teams for AI Security

Recent research exposed how traditional prompt filtering breaks down when attackers use more advanced techniques. For example, multi-step obfuscation attacks were able to slip past 75% of supposedly "secure" LLMs in a recent evaluation—just one illustration of how these filters struggle under pressure. From our side in OffSec, we’re seeing how the move to AI expands the attack surface far beyond what’s covered by standard penetration testing. Risks like prompt injection, data poisoning, and model jailbreaking need red teamers to go beyond the usual playbook. Effective AI red teaming comes down to a few things: ➡️ You need offensive security chops combined with enough understanding of AI systems to see where things can break. That’s often a rare combo. ➡️ Testing should include everything from the data used to train models to how systems operate in production—different weak points pop up at each stage. ➡️ Non-technical threats are coming in strong. Social engineering through AI-powered systems is proving easier than classic phishing in some cases. Right now, a lot of security teams are just starting to catch up. Traditional, compliance-driven pen tests may not scratch the surface when it comes to finding AI-specific weaknesses. Meanwhile, threat actors are experimenting with their own ways to abuse these technologies. For leadership, there’s no sense waiting for an incident before shoring up your AI defenses. Whether you’re upskilling your current red team with some focused AI training, or bringing in specialists who know the space, now’s the time to build this muscle. Cloud Security Alliance has just pushed out their Agentic AI Red Teaming Guide with some practical entry points: https://lnkd.in/ebP62wwg If you’re seeing new AI risks or have had success adapting your security testing approach, which tactics or tools have actually moved the needle? #Cybersecurity #RedTeaming #ThreatIntelligence

As Agentic AI systems move from experimental labs into real-world applications, the need for specialized security testing is no longer optional but urgent. Unlike traditional AI models, Agentic AI operates with autonomy: planning, reasoning, taking multi-step actions, invoking tools, and in some cases, interacting with other agents or external systems. This increased autonomy introduces an entirely new set of risks that existing red teaming frameworks weren’t designed to detect. That’s why the release of the Agentic AI Red Teaming Guide, led by the Cloud Security Alliance and OWASP AI Exchange, is such an important step forward for the field. Shared and spearheaded by Ken Huang, CISSP, this guide outlines 12 critical threat categories specific to agentic systems, including: - Agent Authorization and Control Hijacking – Exploiting misconfigured permissions and elevation paths - Agent Memory and Context Manipulation – Inserting or poisoning memory to change long-term behavior - Goal and Instruction Manipulation – Nudging agents into misaligned or unsafe objectives - Agent Hallucination Exploitation – Leveraging reasoning errors to generate unsafe actions - Multi-Agent Exploitation – Manipulating coordination between agents to create cascading effects - Supply Chain and Dependency Attacks – Targeting models, plugins, APIs, or toolchains the agent relies on - Agent Critical System Interaction – Forcing unsafe or high-stakes interactions with real-world systems - Agent Knowledge Base Poisoning – Altering decision-making by tampering with training data or tools - Agent Impact Chain and Blast Radius – Exploiting systems where agents can trigger downstream effects across applications - Agent Untraceability – Operating in ways that are opaque, unlogged, or unaccountable - Checker-Out-of-the-Loop – Bypassing or overwhelming human-in-the-loop fail-safes - Resource and Service Exhaustion – Overloading compute, API limits, or memory through uncontrolled loops The guide offers practical red teaming techniques, prompts, and methods for testing autonomous agents across planning, execution, tool invocation, and post-action logging stages. It also introduces a structured model for agent red teaming, helping teams assess agent behavior from ideation through deployment. This guide aims to helps operationalize safety testing for a fast-evolving category of systems that are often rushed into production before robust controls are in place. * * * As Ken Huang, CISSP announced in his post (https://lnkd.in/gJ2X7WZ5), the team is kicking off bi-weekly working sessions to explore an open-source tool for using this guide. 🗓 Next meeting: Monday, June 2 at 11:00AM ET 🔗 https://lnkd.in/ds3E3ySa Link to report: https://lnkd.in/giw8MGRK

The Asana AI breach wasn't a surprise. It was an inevitability. For any organization bolting generative AI features onto a traditional security paradigm, a breach isn't a matter of if, but when. The post-mortems will point to a specific vulnerability, but they'll miss the real cause: a fundamental failure to understand that AI introduces an entirely new dimension of risk. Your firewalls, code scanners, and intrusion detection systems are looking in the wrong place. The Asana incident is a textbook example of the principles I detail in my book, Red Teaming AI. The vulnerability wasn't in a line of code a SAST scanner could find. It was systemic. It lived in the data, the model's emergent behavior, and the sprawling MLOps pipeline that connects them. Attackers don't see your siloed tools; they see an interconnected graph. They exploit the seams. Was the breach... 1. A Data Poisoning attack that created a hidden backdoor in the model months before it was ever activated? (See Chapter 4) 2. An Indirect Prompt Injection that turned the AI into a "confused deputy," tricking it into exfiltrating data using its own legitimate permissions? (See Chapter 8) 3. A Software Supply Chain compromise that trojanized the model artifact itself, bypassing every code review and functional test? (See Chapter 9) A traditional pentest would have missed all three. The hard truth is that you cannot buy your way out of this problem. AI security is not a product you install; it is a capability you must build. It requires a cultural shift towards an adversarial mindset. Stop waiting for the next "unexpected" breach. The solution is to move from reactive cleanup to proactive assurance. It's time to invest in dedicated AI Red Teams and embed adversarial testing directly into the AI development lifecycle. Your biggest AI vulnerability isn't in your code; it's in the limits of your adversarial thinking. #AISecurity #CyberSecurity #RedTeaming #AdversarialML #MLOps #LLMSecurity #PromptInjection #CISO #InfoSec #RiskManagement #RedTeamingAI

AI Security cannot be Hope based - it needs a strategy that includes Red Teaming. 🛡️ Microsoft researchers have proposed a comprehensive AI red teaming framework, built from testing 100+ GenAI products. This framework blends a structured threat model ontology with real-world lessons, offering a practical, implementable approach to AI security assessments. For organizations, research institutions, and governments, this is a valuable resource to strengthen AI risk evaluations and address real-world vulnerabilities. Red teaming isn't optional in current threat landscape, it's essential. 👉 How is your organization approaching AI security testing?