ETTA bypasses LLM safety refusals by learning a linear toxicity direction in the embedding space and attenuating it in word embeddings at inference time.
Poisoning Attacks against Recommender Systems: A Survey
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
Modern recommender systems (RS) have seen substantial success, yet they remain vulnerable to malicious activities, notably poisoning attacks. These attacks involve injecting malicious data into the training datasets of RS, thereby compromising their integrity and manipulating recommendation outcomes for gaining illicit profits. This survey paper provides a systematic and up-to-date review of the research landscape on Poisoning Attacks against Recommendation (PAR). A novel and comprehensive taxonomy is proposed, categorizing existing PAR methodologies into three distinct categories: Component-Specific, Goal-Driven, and Capability Probing. For each category, we discuss its mechanism in detail, along with associated methods. Furthermore, this paper highlights potential future research avenues in this domain. Additionally, to facilitate and benchmark the empirical comparison of PAR, we introduce an open-source library, ARLib, which encompasses a comprehensive collection of PAR models and common datasets. The library is released at https://github.com/CoderWZW/ARLib.
citation-role summary
background 1
citation-polarity summary
fields
cs.CL 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
Circumventing Safety Alignment in Large Language Models Through Embedding Space Toxicity Attenuation
ETTA bypasses LLM safety refusals by learning a linear toxicity direction in the embedding space and attenuating it in word embeddings at inference time.