Yellow Teams Analysis: Glasswing Blends AI Attack, Defense

Security has always loved color coding its anxieties. Red teams break things, blue teams defend things, purple teams make everyone admit the meeting could have been a ticket. AI is now kicking that tidy wall chart in the shins, because the same discipline that tests an AI system can also teach teams how to defend it. That is the useful signal in Dark Reading’s report on Anthropic’s Project Glasswing and its invitation to more than 50 organizations. The story is not that every security team needs a new hoodie color by Friday. It is that some teams are treating AI offense and AI defense as a single engineering loop, not two departments exchanging PDFs like diplomatic prisoners. In breach terms, this is the part where the postmortem happens before the incident, which is rude to tradition but kinder to users.

What Dark Reading found inside the yellow team shift

Dark Reading’s Nate Nelson reports that a small number of engineering teams are building both defense and attack tools to test artificial intelligence as a cybersecurity capability and as a threat. The same report anchors that trend around Anthropic’s Project Glasswing, which invited more than 50 organizations. That number matters, not because it proves mass adoption, but because it shows the experiment is being opened beyond one lab’s whiteboard séance. The yellow team idea, as Dark Reading frames it, sits between the classic offensive and defensive tracks. Instead of waiting for one group to simulate an attack and another to translate the rubble into controls, yellow teams build the attack framework and the defense machinery together. The work is less like a staged breach exercise and more like a pressure chamber: create the thing that can hurt you, watch how it behaves, then use that evidence to harden the system before production does its usual unpaid penetration test. That is especially relevant for AI systems because their failure modes do not always look like ordinary software bugs. Models, data pipelines, prompts, inference workflows, and security operations tooling can all become part of the risk surface. Dark Reading’s core point is that the teams closest to these systems are beginning to test AI’s potential for protection and misuse at the same time, which is refreshingly adult for an industry that still sometimes treats logging as a personality flaw.

Why arXiv’s offensive security framing fits the moment A paper on arXiv,

Offensive Security for AI Systems: Concepts, Practices, and Applications, argues that traditional defensive measures can fall short against the unique and evolving threats facing AI driven technologies. The paper presents offensive security for AI as a proactive framework, using threat simulation and adversarial testing to find vulnerabilities across the AI lifecycle. It names techniques such as weakness and vulnerability assessment, penetration testing, and red teaming as ways to uncover risks before they become someone else’s incident report. That maps neatly onto the yellow team model. If adversarial testing reveals critical insights that inform stronger defensive strategies, as the arXiv paper says, then separating the testers from the builders can slow learning. The point is not to abolish specialization. The point is to make the offensive findings immediately useful to the people building detections, controls, safer workflows, and deployment guardrails. Threat actor motivation here is not mysterious character development. If AI systems become common in critical operations, attackers will look for leverage in the parts that are novel, misunderstood, or wired into too much authority. Yellow teaming tries to shorten the time between discovering that leverage and removing it, which is basically patch notes with fewer fireworks and more dignity.

What the breach breakdown says before there is a

breach Dark Reading’s report is useful because it describes a preventive pattern rather than a cleanup ritual. The asset at risk is not just a model, but the organization’s trust in model assisted work: how systems are tested, where data flows, what tools can do, and how quickly defenders learn from offensive exercises. The likely exposure is operational uncertainty, meaning teams may not know which AI behaviors are safe, which are brittle, and which are merely waiting for a threat actor with patience and coffee. The containment move is cultural as much as technical. Treat AI attack tooling as part of the defense build process, with clear authorization, documentation, and repeatable tests. Treat AI defenses as hypotheses that must survive adversarial testing, not as shrine objects blessed by a vendor demo. And please, for the love of every breach notification inbox, write down what worked and what failed so the next test starts smarter. What it actually means for you: if your organization is adopting AI in security operations, product features, or internal workflows, do not put red team findings on one track and defensive engineering on another. Build a loop where offensive tests feed directly into mitigations, monitoring, and safer deployment choices. Watch Project Glasswing and similar efforts for practical patterns, because the future of AI security may belong to teams that can build the lock and pick it in the same afternoon.