RAXE Labs Research Radar decodes AI security papers

This is useful because AI security research is often too dense to act on directly, and the digest is trying to become the translation layer between arXiv and operators. The ratings and badges are the real product here: they turn a paper firehose into a triage queue.

• –`Cascade` makes the strongest point of the issue: securing the model is not enough if the surrounding stack still has exploitable software or hardware weaknesses.
• –`LAMLAD` is a bigger warning than the evasion number suggests, because LLMs are now automating the tedious parts of adversarial ML and lowering attacker skill requirements.
• –`OpenClaw` reinforces that agent risk lives in tool execution and orchestration, not just prompt text.
• –The visible `[VERIFY]` guardrail and source links add credibility in a space where summaries often overclaim.
• –Free access and no signup lower friction, which should help it reach the practitioners who actually need the signal.