Scaling Laws for Adversarial Attacks on Language Model Activations
This reveals a new attack surface for multi-modal and retrieval models, posing a security risk for systems using activations from additional data sources.
The paper tackles the problem of adversarial attacks on language models by manipulating activations, showing that controlling a small subset of activations can predictably influence up to 1000 subsequent tokens, with a linear scaling law and constant attack resistance across model sizes.
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 $t_\mathrm{max} = κa$. 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 $χ$) 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.