There is a lot of AI security research being published on arXiv that has real-world implications, but most of it is written for other researchers. We started a bi-weekly digest that translates these papers into something practitioners and anyone interested in AI safety can actually use. Each paper gets a structured rating across four dimensions (Threat Realism, Defensive Urgency, Novelty, Research Maturity) and a badge: Act Now (immediate practical concern), Watch (emerging technique to monitor), or Horizon (longer-term research trend). **First issue highlights:** **Cascade -- "What if attackers combined software bugs with hardware attacks against AI systems?"** Researchers demonstrated that compound AI systems (the kind built from multiple components -- a retrieval system, an LLM, a database, tools) inherit the vulnerability surface of every component. They showed attacks that chain traditional software CVEs with hardware-level exploits like Rowhammer against AI infrastructure. The practical implication: securing the LLM is not enough if the system around it is vulnerable. **LAMLAD -- "LLMs that automate attacks against other ML systems"** A dual-LLM agent system that automates adversarial machine learning attacks against Android malware classifiers, achieving a 97% evasion rate. The significant part is not the evasion rate itself -- it is that LLMs can now automate the tedious parts of adversarial ML that previously required specialised expertise. This lowers the barrier to attack substantially. **OpenClaw -- "Your AI agent framework probably has these four types of vulnerabilities"** Identifies four classes of vulnerabilities in autonomous agent frameworks. The finding that matters: most current defences focus on the prompt layer, but the real attack surface is in the execution and tool-use layer. Every claim in the digest links back to the source arXiv paper. We flag anything that could not be verified with a visible [VERIFY] tag. Free, no paywall, no signup: https://raxe.ai/labs/radar [link] [comments]
