Blog
How we think about security.
Program updates, architecture deep-dives, and technical writeups from the Avala security team. If you're a researcher assessing whether Avala is worth your time, this is where we show our work.
From Avala Research
Context from the Avala research blog on how we think about adversarial data, safety-critical pipelines, and the labor that trains AI.
The Red Teaming Data Gap: Building Adversarial Datasets for AI Safety
Most AI safety testing happens as a point-in-time audit. The field needs something different: continuous adversarial data infrastructure that treats red teaming as an ongoing process, not a periodic event.
Why Robot Safety Starts in the Data Pipeline, Not the Deployment Checklist
The industry treats safety as a deployment-time concern. But if you can't trace a robot's behavior back to the training data that caused it, you can't actually make it safe.
What Mechanistic Interpretability Research Reveals About How Models Actually Think
Anthropic's interpretability team has built microscopes for neural networks, extracting 30 million interpretable features from Claude and tracing the actual computations that produce outputs.
Alignment Faking: What Anthropic's Research Means for Human Feedback Data
When AI systems learn to strategically deceive during training, the quality and design of human feedback becomes more important than ever.