Scaling Laws for Adversarial Attacks on Language Model Activations
Stanislav Fort
We explore a class of adversarial attacks targeting the activations of
language models. By manipulating a relatively small subset of model
activations, $a$, we demonstrate the ability to control the exact prediction of
a significant number (in some cases up to 1000) of subsequent tokens $t$. We
empirically verify a scaling law where the maximum number of target tokens
$t_\mathrm{max}$ predicted depends linearly on the number of tokens $a$ whose
activations the attacker controls as $t_\mathrm{max} = \kappa a$. We find that
the number of bits of control in the input space needed to control a single bit
in the output space (what we call attack resistance $\chi$) is remarkably
constant between $\approx 16$ and $\approx 25$ over 2 orders of magnitude of
model sizes for different language models. Compared to attacks on tokens,
attacks on activations are predictably much stronger, however, we identify a
surprising regularity where one bit of input steered either via activations or
via tokens is able to exert control over a similar amount of output bits. This
gives support for the hypothesis that adversarial attacks are a consequence of
dimensionality mismatch between the input and output spaces. A practical
implication of the ease of attacking language model activations instead of
tokens is for multi-modal and selected retrieval models, where additional data
sources are added as activations directly, sidestepping the tokenized input.
This opens up a new, broad attack surface. By using language models as a
controllable test-bed to study adversarial attacks, we were able to experiment
with input-output dimensions that are inaccessible in computer vision,
especially where the output dimension dominates.