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Activation Atlases expose neural-network bugs and human-designed attacks

Chris Olah and Ludwig Schubert released activation atlases and an interactive demo for auditing neural networks. The method exposed spurious correlations and enabled human-designed attacks that fooled tested vision models as often as 93 percent.

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CURRENT ASSESSMENT · REVISION 1
AWAY FROM DOOM27confidence 88/100

Why it moved the index

The released visualization method and demo produced concrete audit findings rather than a purely theoretical proposal. Its direct evidence came from vision models, so the magnitude is limited relative to later production language-model interpretability work.

Assessment history

  1. R1
    Away 27 · confidence 88

    Adds a previously missing pre-2020 Chris Olah result with a dated primary release, public tooling and demonstrated model-audit impact.

    14 Aug 2026

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  1. DoomBench assesses “Activation Atlases expose neural-network bugs and human-designed attacks” as evidence moving away from doom, with magnitude 27 and confidence 88 out of 100 in the safety and alignment category.

  2. The DoomBench assessment of “Activation Atlases expose neural-network bugs and human-designed attacks” is based on reporting from OpenAI and records the editorial rationale, source quality, attribution, and revision history.

  3. DoomBench summarizes “Activation Atlases expose neural-network bugs and human-designed attacks” as follows: Chris Olah and Ludwig Schubert released activation atlases and an interactive demo for auditing neural networks. The method...

    https://www.doombench.com/news/activation-atlases-expose-neural-network-bugs-and-human-designed-attacks-2019-03-06