Reasoning Introduces New Poisoning Attacks Yet Makes Them More
  Complicated
Authors: name: Hanna Foerster, name: Ilia Shumailov, name: Yiren Zhao, name: Harsh Chaudhari, name: Jamie Hayes, name: Robert Mullins, name: Yarin Gal

Early research into data poisoning attacks against Large Language Models
(LLMs) demonstrated the ease with which backdoors could be injected. More
recent LLMs add step-by-step reasoning, expanding the attack surface to include
the intermediate chain-of-thought (CoT) and its inherent trait of decomposing
problems into subproblems. Using these vectors for more stealthy poisoning, we
introduce ``decomposed reasoning poison'', in which the attacker modifies only
the reasoning path, leaving prompts and final answers clean, and splits the
trigger across multiple, individually harmless components.
  Fascinatingly, while it remains possible to inject these decomposed poisons,
reliably activating them to change final answers (rather than just the CoT) is
surprisingly difficult. This difficulty arises because the models can often
recover from backdoors that are activated within their thought processes.
Ultimately, it appears that an emergent form of backdoor robustness is
originating from the reasoning capabilities of these advanced LLMs, as well as
from the architectural separation between reasoning and final answer
generation.