TL;DR: Using dynamic agentic simulations, we found that in the majority of tested scenarios, AI agents do not push back on breaking EU law to achieve their goals, including the provisions the EU ranks as most serious. Initial results show that leading commercial AI models break European law in up...
TL;DR: We replicated the animal welfare scenario from Anthropic's Alignment Faking paper across six generations of Claude models using 125 prompt perturbations. Sonnet 4.5 verbalizes alignment-faking reasoning 6.6 times more often than its predecessor Sonnet 4. The newly released Opus 4.6 rarely verbalizes alignment faking in its reasoning, but still...
TL;DR: Safety prompts are often used as benchmarks to test whether language models refuse harmful requests. When a widely circulated safety prompt enters training data, it can create prompt-specific blind spots rather than robust safety behaviour. Specifically for Qwen 3 and LlaMA 3, we found significantly increased violation rates for...