We built a free digest that translates AI security research papers into plain language -- first issue covers cross-stack attacks on compound AI systems and LLMs automating their own adversarial attacks

*AI Security Research in Plain Language: Our New Bi-Weekly Digest*

There's a wealth of AI security research being published on arXiv, but most of it is written in a language that's inaccessible to practitioners and anyone interested in AI safety. We've started a bi-weekly digest to change that. Each issue translates the latest research into plain language, providing a structured rating and badge for each paper.

First Issue Highlights

The first issue of our digest covers two significant papers that have real-world implications for AI security. The first paper, "Cascade," explores the concept of combining software bugs with hardware attacks against AI systems. Researchers demonstrated that compound AI systems, which are built from multiple components, inherit the vulnerability surface of every component. This means that securing the Large Language Model (LLM) is not enough if the system around it is vulnerable to attacks.

The second paper, "LAMLAD," introduces a dual-LLM agent system that automates adversarial machine learning attacks against Android malware classifiers. The system achieved a 97% evasion rate, but the significant part is not the evasion rate itself – it's that LLMs can now automate the tedious parts of adversarial ML that previously required specialized expertise. This lowers the barrier to attack substantially.

What You Need to Know

Our digest is designed to provide actionable insights for practitioners and anyone interested in AI safety. Each paper is rated across four dimensions: Threat Realism, Defensive Urgency, Novelty, and Research Maturity. We also assign a badge to each paper: Act Now (immediate practical concern), Watch (emerging technique to monitor), or Horizon (longer-term research trend). The first issue highlights the importance of securing not just the LLM, but the entire system around it, and the potential for LLMs to automate attacks against other ML systems.

A Free Resource for the Community

Our digest is free, with no paywall or signup required. We believe that AI security research should be accessible to everyone, not just researchers. Every claim in the digest links back to the source arXiv paper, and we flag anything that could not be verified with a visible [VERIFY] tag.

The first issue of our digest is available now, and we invite you to explore the latest research in AI security. We'll be publishing new issues bi-weekly, so be sure to check back for the latest insights and analysis.