LidaRed: ControlArena Red-Team Dataset Generator

Here is a list of my thoughts:

1. I'm unsure of the problem you are trying to solve with this project. You have generated some trajs from the Control Arena benchmark. I'm unsure what value that provides.

2. Given that the value is a dataset, the dataset itself seems very limited.

3. I really like that the write-up is clean and well presented.

4. In the abstract, you state you "iteratively refined attacks using monitor feedback, with a focus on omission-based attacks", However, this is not talked about in your methods. This suggests to me that the method lacks the detail needed if someone was to replicate your work.

5. You state a key finding about omission-based attacks being hard to detect; however, I'm unsure how you proved this from your results. This could just be because of the data set you used, or randomness instead. If you don't have hard evidence (as that's hard to get in a hackathon), then maybe saying these findings in a discussion may be more appropriate.

Given that, good work on making a paper in 3 days! It's no easy task.

- clear code and clearly written, interesting idea simply and effectively presented, enjoying reading it ;)

- further comments intended to think of what this would look like as a full paper, and where the research direction seems to point to.

- iterative improvements are great, but there’s no discussion of the actual rewrites that are made to the prompts in order to bypass the monitor - this would be very interesting to look at. a simple case study of a couple prompts would be great. the framework itself seems novel well suited to current trends / agentic use of LMs

- makes sense that omission in the networking example is harder to detect - you're testing the model's conception of an "ideal configuration" here, or the ability to construct a configuration - so you should first establish if this is something that the model knows at all, or some baseline for what safety looks like in your network scenario. for Claude Opus I imagine that this is reasonable to assume that this is known, but it should still be tested

- what’s the so what? this reads like a SWE scaffold to more effectively bypass safety screens. Iterating from feedback is not novel, though finding specific techniques that work is. How could this be meaningfully used to improve existing safety regimes - is this for refining evals?