Indirect prompt injection attacks exploit a fundamental weakness of LLMs: executing unauthorized tasks from untrusted sources. Despite the practical significance of this attack vector, existing benchmarks rely on small sets of template-based attacks (100–1,000 examples) that fail to capture the diversity and adaptiveness of real-world adversaries. Consequently, benchmarks are saturated and overfit to static attacks. We introduce LLMail-Inject, a large-scale benchmark of 208,095 distinct human-written attack prompts collected through a public red-teaming challenge with 839 participants. LLMail-Inject evaluates the attack’s complete chain of execution: prompts must be retrieved from an email database, evade production-level defenses, and trigger unauthorized tool calls with correct arguments. Evaluation reveals that models that achieve <5% attack success rate (ASR) on existing benchmarks fail more on LLMail-Inject: Meta SecAlign–specifically trained against prompt injection–still yields 32% ASR, GPT-4o exhibits 58% ASR, and GPT-5 mini 47% ASR. We further demonstrate that attacks transfer across domains, achieving 37% ASR on proof-of-concept coding tasks and 28-45% ASR on file management tasks. We release our challenge platform and curated evaluation subsets at multiple difficulty tiers and the full dataset with rich annotations.

Large Language Models (LLMs) have shown remarkable advancements in specialized fields such as finance, law, and medicine. However, in cybersecurity, we have noticed a lack of open-source datasets, with a particular lack of high-quality cybersecurity pretraining corpora, even though much research indicates that LLMs acquire their knowledge during pretraining. To address this, we present a comprehensive suite of datasets covering all major training stages, including pretraining, instruction fine-tuning, and reasoning distillation with cybersecurity-specific self-reflection data. Extensive ablation studies demonstrate their effectiveness on public cybersecurity benchmarks. In particular, continued pre-training on our dataset yields a 15.9% improvement in the aggregate score, while reasoning distillation leads to a 15.8% gain in security certification (CISSP). We will release all datasets and trained cybersecurity LLMs under the ODC-BY and MIT licenses to encourage further research in the community.