Read the latest insights on LLM security, best practices, and industry trends
We audited dozens of enterprise AI deployments in Q4. Teams relying solely on native model safety failed 90% of our agentic attacks. Here's what a proper defense architecture looks like.
We see the same two flawed guardrail strategies across enterprise AI deployments. Both fail in different ways—and both are worse than they appear.
As AI agents take on autonomous roles in security pipelines, indirect prompt injection becomes a pipeline-level threat. In our red team exercises, 4 out of 10 attacks successfully manipulated AI security assistants into clearing vulnerable code.
OpenAI's Lockdown Mode restricts what ChatGPT can do in order to reduce security risk. That tradeoff reveals a deeper problem with how AI security is being approached.
Most security failures in LLM deployments aren't sophisticated attacks—they're preventable mistakes. Here are seven we see repeatedly.
Untrusted LLM providers can silently inject credential exfiltration and backdoors into generated code. We demonstrated this with two model sizes. Both complied.
