Prompting techniques that mark untrusted input so the model can tell data from instructions.
Nasr, Carlini, Tramèr and 11 co-authors apply gradient, reinforcement learning, search and human red-teaming attacks to 12 published defenses. Most defenses originally reported near-zero attack success, but the adaptive attacks exceed 90% success against most, and human red-teamers succeeded on every challenge in the subset of defenses they were given.
Microsoft researchers and collaborators report on LLMail-Inject, a public challenge in which participants tried to inject instructions into emails to trigger unauthorized tool calls by an LLM email assistant protected by various defenses. The released dataset contains 208,095 unique attack submissions from 839 participants across multiple defenses, models and retrieval configurations.
Shi and colleagues describe Google DeepMind's continuous adaptive-attack evaluation of Gemini against indirect prompt injection in tool-use settings. On Gemini 2.0, adaptive attacks generally matched or beat non-adaptive ones against eight baseline defenses, reaching 98.4% against in-context learning and 82.4% against spotlighting, while a warning defense and a user-instruction classifier held (at most 10.8% and 3.0%). Adversarial fine-tuning for Gemini 2.5 lowered but did not eliminate attack success.
Hines and colleagues at Microsoft describe spotlighting, a family of prompt-engineering transformations (delimiting, datamarking, encoding) that signal to the model where untrusted text came from. On GPT-family models they report attack success falling from above 50% to under 2% with minimal task impact. Microsoft later described spotlighting as one layer of its production defense-in-depth.