The most damaging LLM flaws rarely stop at an unsafe answer. They cross into retrieval systems, identity controls, tools, ...

Shopify's engineering introduced gisting, a novel technique for compressing long LLM prompts into a smaller set of learned "gist" tokens, improving throughput and reducing inference cost.

In the past two years, businesses have been trying to fit large language models (LLMs) into support, analytics, development, and internal automation like never before. Along with the increasing ...

In Part 1, we established why LLMs are vulnerable: the attention mechanism treats all input tokens equally, with no architectural separation between trusted instructions and untrusted user data. Now ...

Prompt injections, the malicious commands attackers embed into content to entice large language models to follow them, have been attackers’ go-to tool for turning AI platforms against their users. A ...

Large language models are supposed to shut down when users ask for dangerous help, from building weapons to writing malware. A new wave of research suggests those guardrails can be sidestepped not ...

The GRP‑Obliteration technique reveals that even mild prompts can reshape internal safety mechanisms, raising oversight concerns as enterprises increasingly fine‑tune open‑weight models with ...

One of the coolest things about generative AI models — both large language models (LLMs) and diffusion-based image generators — is that they are "non-deterministic." That is, despite their reputation ...

When should you use an LLM over a statistical model? Three real-world cases reveal how data, representation, and training ...