AI red teaming
Red teaming an LLM application means testing the workflow: retrieval, memory, tools, and anything that can leave the system.
One evergreen method sits next to the attacker-versus-defender notes and the technique pieces that are actually a different test.
AI Red Teams Are Standardizing on Structured Output Attacks
Attackers are no longer just trying to jailbreak a model’s text—they’re targeting the JSON, XML, and function-call formats that modern AI systems trust downstream. Security teams need to understand how structured outputs can silently turn a harmless-looking response into unsafe automation or data leakage.
LLM Red Teaming Is Shifting Toward Multi-Turn Jailbreaks
Static prompt filters catch obvious attacks, but newer jailbreaks chain roleplay, context poisoning, and tool abuse across several turns to slip past them. Security teams now need red-team tests that measure how models behave over an entire conversation, not just one prompt.
AI Red Teaming for LLM Apps: Why Prompt Injection Still Beats Guardrails
Prompt injection is still one of the easiest ways to make LLM apps leak data or ignore policy, especially when tools, retrieval, and memory are wired together. This post shows what modern AI red teaming needs to test—and why static guardrails fail in real deployments.
AI Red Teaming: Break Your LLM Before Attackers Do
A structured red team should test four things in order: threat model, adversarial prompts, tool-abuse paths, and output validation gaps. This post shows a repeatable methodology for finding the failure modes attackers are most likely to exploit in 2026.