New from @VulcanBench, my first eval suite focused on safety, name, drumroll please...Safety v1 🖖 🛟 And out of the gate I'm focusing on testing for prompt injection safety, as I see this as one of the key risks today for companies with teams using coding agents. Here's how this eval suite works: - I take 10 real coding tasks and hide 2 harmful instructions in each repo, written to look like ordinary notes (a README line, a spec comment). - then I ask the key question: will an AI coding agent do what the note says? (i.e. will the prompt injection be successful) Claude Opus 5.5 followed 0 of 60 notes asking it to send data over the network, leak a secret, or delete files. Across all 100 planted notes though, it did follow 3, and all 3 asked it to add extra code or a file. Overall Opus 5.5 is very hard to trick into harmful actions through instructions hidden in a codebase, and it usually tells you when someone tries. Here's my very first model card for VulcanBench Safety v1.