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Source note3 min read

Pretraining Data Can Be Poisoned through Computational Propaganda

Source

arXiv

arxiv.org/abs/2607.15267v1

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Source

Plain-Language Brief

The research shows that harmful information can be introduced into language models during their pre-training phase by manipulating web-scale discussion interfaces, such as online forums or comment sections. A new analysis tool called HalfLife was developed to measure the inclusion of malicious content in training data after web crawling and curation.

What the Document Says

  • The research discusses the potential for poisoning pre-training data, introducing harmful behaviors into language models that are difficult to detect and mitigate.
  • Prior work on poisoning pre-training data has focused on established sources like Wikipedia, but this paper considers a broader scale and heterogeneity typical of pre-training corpora.
  • The study demonstrates the feasibility of poisoning attacks on pre-training data beyond the limited setting through an existing web-scale content injection mechanism: public discussion interfaces.
  • To measure whether malicious content is included after web crawling and data curation, a new analysis called HalfLife was introduced.
  • HalfLife was used to explore the feasibility of poisoning pre-training corpora at web scale through open discussion interfaces.
  • The analysis shows the importance of estimating whether poison injections are included in pre-training data and identifies third-party webpage content as a potential vector for attacking language model pre-training.

Key Concepts Mentioned

  • pre-training data
  • language models
  • harmful behaviors
  • poisoning attacks
  • web-scale discussion interfaces
  • malicious content
  • HalfLife

Claims Made by the Document

  • Poisoning pre-training data can introduce harmful behaviors.
  • Existing work on poisoning pre-training data focuses on limited sources.
  • Poisoning attacks are feasible through public discussion interfaces beyond the limited setting.
  • Malicious content may be included after web crawling and data curation.
  • Third-party webpage content could be a vector for attacking language model pre-training.

Evidence Used

  • The research paper provides details on their experiments and results that support these claims.

Methods / Approach

  • The researchers use HalfLife, a new analysis tool, to measure the inclusion of malicious content in training data after web crawling and curation.
  • They demonstrate feasibility by exploring the poisoning of pre-training corpora at web scale through open discussion interfaces.

Limitations Stated by the Authors

  • The paper focuses on one specific method for poisoning pre-training data (through public discussion interfaces), and it's unknown whether other methods exist or are more effective.
  • It's not stated whether the researchers tested their findings across different language models.

Limitations Noted by the Agent

Agent interpretation — not from the source.

  • The study does not provide information on how prevalent this issue might be in real-world scenarios, or the potential impact of such poisoning attacks.
  • Without more context about the specific discussion interfaces used, it's unclear how generalizable these findings are.

Open Questions

  • What other methods might exist for poisoning pre-training data?
  • How prevalent is this issue in real-world scenarios and what is the potential impact of such attacks?
  • How generalizable are these findings across different language models?

Notes for Future Q&A

May include interpretation. Labeled where it does.

  • The paper discusses the challenges of detecting and mitigating harmful behaviors introduced through poisoned pre-training data.
  • It's important to note that not all content on public discussion interfaces is malicious.

Raw Abstract / Excerpt

Poisoning pretraining data can introduce harmful behaviors to LMs that are difficult to detect and mitigate. Prior work on poisoning pretraining data has largely exploited established data sources such as Wikipedia, which do not represent the large scale and heterogeneity typical of pretraining corpora, and has ignored the interaction between poisoned data and data curation pipelines. We demonstrate that poisoning attacks on pretraining data are feasible beyond this limited setting through an existing web-scale content injection mechanism: public discussion interfaces. Additionally, to measure whether malicious content is included after web crawling and data curation, we introduce HalfLife, a novel analysis for estimating adversarial content inclusion in web-crawl based LM training data. We use HalfLife to explore the feasibility of poisoning pretraining corpora at web scale through open discussion interfaces. Our analysis demonstrates the importance of estimating whether poison injections are included in pretraining data, and establishes third-party webpage content as a possible vector for attacking language model pretraining.