Direction, evidence grade, and study type are AI-generated labels (gpt-5-mini), not human-verified. Syntheses are LLM-written. "Tensions" are machine-detected candidates, not confirmed contradictions. A research-acceleration tool, not peer review.
How this is built →
1Distinct papers
5Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2367796352
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Nicolas Papernot (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Governance: 1 paper
- Innovation: 1 paper
Claim outcomes
- Ai Safety And Ethics: 1 paper
- Governance And Regulation: 1 paper
Papers in the Semantic Scholar view
Latest stored Semantic Scholar author observations only. Citation counts below are from the same provider and are not combined with other services.
Scroll the table horizontally to see every column.
| Paper | Author evidence | Date | Provider citations |
|---|---|---|---|
| AI-powered worms can use stolen compute on infected machines to run open large language models and craft target-specific attacks, potentially lowering marginal infection costs to nearly zero; the approach circumvents centralized platform safety controls and could create an asymmetric advantage for attackers unless defenses and governance adapt.arxiv | Nicolas Papernot provider id |
2026-06-02 | 1 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 1 cumulative citations. This is a coverage summary, not an author score or h-index.