Claim · 2023-10-25 · publication · Stated in source · Supported

Gopal and co-authors' paper of 25 October 2023 states that its results suggest that releasing the weights of advanced foundation models, no matter how robustly safeguarded, will trigger the proliferation of knowledge sufficient to acquire pandemic agents and other biological weapons.

One recorded claim. The claim panel gives its qualification, the source passages with their place in each source, and how it was checked.

What it connects

Will releasing the weights of large language models grant widespread access to pandemic agents? → contests → Open models reduce risk

Where it appears

Arguments about catastrophic risk from advanced AI →

Claim and evidence

Will releasing the weights of large language models grant widespread access to pandemic agents?contests ↓Open models reduce risk

Machine-checked, not reviewed by an editorStated in sourceSupported

Supporting evidence: primary · 1 source

Date: 2023-10-25 · publication Records dated 2023 →
Role: Contesting
Register (kind of text): Nonfiction
Engagement: Engages a premise. Answers the premise that safeguards keep the misuse risk of released weights low, stating that release proliferates such knowledge however robustly the model is safeguarded.

Scope and limitsRead in its abstract only; the paper's method and its comparison with information already available elsewhere were not checked. A statement of the authors' conclusion, not evidence that it is correct.

The evidence 01

primary sourceSupports the claim

Will releasing the weights of large language models grant widespread access to pandemic agents?

arXiv:2310.18233 · 2023-10-25

Cite the original source

Cite the original document, not Selekoa. Check these details against it.

Source excerpt

“Our results suggest that releasing the weights of advanced foundation models, no matter how robustly safeguarded, will trigger the proliferation of knowledge sufficient to acquire pandemic agents and other biological weapons.”

Location in source Abstract

How it was read Retrieved directly from the publisher

Machine-checked · 2026-10-31

Source details and check notes

Authors: Anjali Gopal; Nathan Helm-Burger; Lenni Justen; Emily H. Soice; Tiffany Tzeng; Geetha Jeyapragasan; Simon Grimm; Benjamin Mueller; Kevin M. Esvelt
Version: Retrieved for the candidate pack on 2026-10-29 (SHA-256 76930b27d12c6aac9f02b0bf7e7c0b83b2c8ca0f7840c9ef98e4729cb30894d6). arXiv v1 abstract page (the v1 PDF as retrieved has no text layer); quotation limited to the abstract. The cross-family check retrieved it again on 2026-10-31; the bytes differ, and the check compared the quoted passages.
Canonical address: Open the canonical source ↗
Accessed: 2026-10-29
Language: English (en)
Attribution: Anjali Gopal, Nathan Helm-Burger, Lenni Justen, Emily H. Soice, Tiffany Tzeng, Geetha Jeyapragasan, Simon Grimm, Benjamin Mueller, Kevin M. Esvelt, “Will releasing the weights of large language models grant widespread access to pandemic agents?”, 2023-10-25.
Reuse: No open redistribution licence asserted. Bibliographic metadata, editorial paraphrases and short attributed quotations (at most 50 words each). Recheck terms before wider redistribution.
Check notes: Candidate pack passage, arguments about catastrophic risk from advanced AI (research/risk-debate). The quotation (31 words) matched the text retrieved for the candidate pack on 2026-10-29. Matching ignored differences of whitespace, case, quotation marks, dashes and Unicode compatibility forms, so the source’s typography can differ. No human editorial review.

Status and meaning

Editorial status. Not reviewed by an editor. An exploratory record that no editor has accepted. This status does not say whether a person has read any linked passage. Each evidence record above shows how it was checked.

Relation. A passage of the document disputes the proposition or a premise it depends on. The record documents the text’s position, not who is right.

Date. Date of the arXiv v1 submission.

Record ID rd-r15-gopal-2023-pandemic-agents